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Social Media as a Research Methodology

by Bex Carson , on December 8, 2016

According to the Gartner hype curve for new technologies, a period of hype and inflated expectations is followed by the “trough of disillusionment” during which we become more critical of a new concept or technology we’ve bought into and believed in its inherent value. Questions ensue.

Does it really do what it promised? How do I know I can trust this data? Am I getting ROI out of this? It’s during this latter phase where the new technology has to mature and prove that it is worthy to become a stable, ongoing part of our world. This is where I believe social media is today, and now is the time for social to prove its credentials as a research methodology.

In my experience, it doesn’t take much to convince people that there is huge potential value within social data for understanding consumers more deeply. It’s a bit of a no-brainer, in theory, that all of this unprompted conversation would contain insights into how people think and feel, what they care about, what they’re worried about, and what they need and want.

Any form of consumer research is at best a version of the true experiences of groups of individuals. It’s impossible for any one method to perfectly encapsulate and understand complex human experience. The more sources in your research mix, the more accurate the picture you’re building will become. So, while I would never suggest that social should replace other forms of market research, it would certainly be a missed opportunity to exclude this incredibly rich new data source from your research mix.

Many market researchers have held back from fully embracing a social research methodology or incorporating social into their research mix in a fully integrated way. In my opinion, this is not due to a lack of understanding of the potential opportunity for finding valuable insight, but to the perceived issues and limitations of this type of data.

The rise of insight-driven business has positioned the market researcher at the center of some of the most important strategic decisions that are made in many of the world’s largest companies. To better understand the potential challenges and barriers with adopting social research methodology, I conducted interviews with a number of highly experienced and talented CMI (consumer market insights) professionals. There were two recurring themes within these interviews:

Representation is a concern. Social media data is created by a self-selecting group, which differs from channel to channel and doesn’t necessarily reflect the broader online and offline population. This poses the question: can I use this data to help understand a whole consumer group, or only those I’m hoping to reach via social channels?
Social data is often “sold” as being less biased than other research methods, although in reality, it’s not. Unlike tried-and-tested methodologies, we don’t understand how to reduce the biases within this dataset.

Both are completely valid concerns that resonate strongly with me as a researcher. However, neither should prevent you from using social as a research methodology. Let’s address each issue.

Overcoming Representation Issues

It’s true that social data as a whole is unlikely to fully represent the offline world in the way that a weighted panel can because a selection bias exists within social data. That doesn’t mean that it’s impossible to understand aspects of offline consumers, but it does need to be considered in the methodology.

Just as you wouldn’t serve a survey to everyone in a panel (you select your target audience), you don’t have to listen to everyone on social media all at once. You can select an audience based on many different characteristics, demographics, life stages, and interests. You can then benchmark those audiences to understand how they think differently about different subjects or weight the data from different groups to better model it against the offline population.

Overcoming the Biases in Social

The first stage in mitigating any bias is to acknowledge and understand the problem. Social, like all other forms of research, is vulnerable to bias. We discussed selection bias above, but here are three more stages in your analysis where you should be careful to avoid biases creeping in:

1) Data collection : Boolean search strings are incredibly flexible and allow you to gather and segment data in almost any way, but they are open to bias if proper care and attention are not paid. If you don’t fully research the key terms and phrases for a given topic, all social handles for a brand, relevant hashtags, common misspellings and typos or slang terms that could possibly exist, you could be biasing your data toward people who think and speak like you. Even if you know a topic well, it’s still important to do some desk research to make sure you’re challenging your assumptions to reduce bias in your data collection.

2) Data segmentation : You can segment your data using Boolean by categorizing and benchmarking within your dataset. As with the query, it’s important to invest time into researching topics within your data to avoid skewing it toward trends that you already know about. For a truly data-lead segmentation, the best approach is to take a representative random sample of conversation and code it manually for themes as they emerge. This allows you to discover new topics, reduce the bias inherent in searching for topics you already know about, and get more granular in your themes. A human can detect far more subtleties in tone, emotion, and context, which is where the data is at its most rich and insightful.

3) Interpretation biases: All research, particularly qualitative research, involves interpretation of themes by a human analyst, and so is open to researcher or interpretation bias. Being cognizant of this is the most important factor for reducing its impact. A representative coded sample is also helpful in challenging your assumptions as a researcher. It’s important to remember that you are telling the consumer’s story, not your own.

There is another factor, which I believe is the greatest challenge not only for social data but all forms of business intelligence. The rise of self-serve digital research platforms means that non-CMI professionals can now create and analyze surveys and track web metrics and social data themselves. This presents huge opportunities to truly distribute insights throughout the business and allow us to actually realize the lofty aim of being an insight-driven business.

It also presents a significant challenge. The word “insight” is prone to overuse and as a result, is frequently misunderstood. An insight is not a chart or a number, but rather an idea communicated from one human to another to convey a way you could do something differently, better, or new.

The journey from data to insight, and from insight to action, contains two bridges that are reliant on human interpretation, communication, and understanding. Opening this process up to more humans increases the risk for human error, and for muddying the water of research with opinion.

One approach to combat this is a central CMI team that produces and distributes insight throughout the business, and this can work really well. However, it’s my view that in the long-term the role of the CMI professional isn’t going to be fighting against distributed power to find insight, but will set the guardrails that enable different teams to explore data safely, and generate robust, impactful insights themselves.

When we achieve this, it will be possible to build a fully insight-driven business.

Finding Business Opportunities: The Importance of Market Research

About the Author: Bex Carson is the Global Director of Research Services at Brandwatch .

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Social Media Research: A Comprehensive Guide

Guv Callahan

May 20, 2024

8 min. read

Social media research helps you unlock the potential of social content for business. We’re living in a world where tweets hold power and likes shape perceptions. When you know what to publish and who you’re posting it for, you can construct a stronger strategy that helps you meet key goals.

Data isn’t just about numbers; it’s about uncovering narratives and following the breadcrumbs of likes, shares, and comments to gain deeper understandings. There’s a method to the madness of selfies and status updates. The right approach to social media research helps you learn more about the collective consciousness of society — and use it to your advantage.

Let’s explore the language of social media likes and shares and dig beneath the surface of our digital interactions.

What is Social Media Research?

Tools and techniques for social media research, understanding the difference: social media research vs. traditional research, harnessing the power of social media research for your business, ethics and privacy in social media research, success stories: real world examples of social media research.

experts conducting social media research

Social media research is the process of using social media data to learn about trending topics, audiences, and content performance. Reviewing social data gives you quantitative insights (e.g., engagement rates , best posting times ), but it can also lead to qualitative learnings like human behaviors, preferences, and opinions.

When conducting social media research, companies can look for patterns and sentiments to drive their social media marketing strategy. They can decide what content to create, which channels to post on, how to reach their audience, when to post content, and a myriad of other decisions that will lead to faster results.

putting a magnifying glass on data collected during research

There’s no single best way to do social media research. You can manually review engagement on your posts or look at your competitors’ content. Or you can use third-party social listening tools to aggregate social data for you.

Social media research can be formal (like a traditional research project) or informal. You might have a certain goal in mind, or you might not know what you’re looking for and just want to see what pops up.

Let’s review some options.

Social media analytics

No matter what channels you choose, you can gain a wealth of insights from built-in social analytics. Platforms like Facebook, Instagram, and Twitter give you instant intel about your content performance and audiences.

Even better, you don’t always need to know what you’re looking for. You can start combing through your analytics, then jot down questions or ideas you want to explore further.

Tip: Learn more in our blog The Complete Guide to Social Media Analytics .

Google Alerts

Google Alerts is a free and underrated tool that gives you unique angles and insights on a given topic. You can set up a Google Alert related to a keyword or topic of your choice, then receive a daily digest of articles published on that topic.

From there, you can learn more about what other brands and businesses are publishing. Repurpose your findings into your social media content to get ahead of trends and topics. You can lead conversations instead of joining them after they blow up on social.

Social listening tools

Social listening tools like Meltwater let you be the fly on the wall in the social world. You can “listen” to what your audience is saying and truly be everywhere all at once.

These tools monitor billions of publicly available data points across multiple social channels, like Twitter, Facebook, and Instagram. They help brands track mentions of their products or brand names in real time so you can become part of important conversations.

You can also track topics related to your niche or learn more about what your audience is talking about beyond your brand. This gives you direct insight into their lifestyles so you can meet them where they are authentically.

Want to learn more about how Meltwater could help your social media research? Fill out the form below and an expert will be in touch!

Media intelligence tools

Taking social listening a step further, you can add media intelligence tools to the mix to learn what’s being talked about beyond social media. Meltwater’s media intelligence suite lets you monitor TV and radio channels, blogs, print media, and other new sources around the world.

This gives you more comprehensive insights into hot topics and trends that you can repurpose for social media. News-worthy events make their way to social media, giving you an easy “in” to your audience’s attention.

Aside from the social-specific aspect, social media research holds a few advantages over traditional research.

For starters, social research gives you real-time data that’s constantly changing. You can also get the most specific insights according to your audience and social channels, not just general info. This means you can shorten the research curve and get faster insights about topics that matter to you.

By comparison, traditional research is often a more structured approach with specific goals in mind. It typically requires lots of sources and manual effort. It takes time to find and vet sources, cross-reference data, and ensure a high level of accuracy.

Combining both types of research can give you the most comprehensive view of your audience.

Now that you know what social media research is, let’s explore some ways you can apply it to your business.

Identify your target audience

Analyzing social media data can help you pinpoint who your target audience is (because it’s not always who you think). You might have your audience defined on the surface with basics like age, gender, and geographic location, but social research can dig several layers deeper to uncover new audience segments you haven’t considered.

Audiences evolve all the time. Their preferences, needs, and interests change. This means that who you want to reach today might not be the same person you want to connect with in the future. Constantly finding new things about your audience will help you continue generating content that captures their interests.

Improve brand reputation

Monitoring online conversations and feedback gives companies a direct path to reputation management . You can more easily spot when trouble might be brewing so you can act fast and defend against hits to your brand image.

Proactively engaging with customers on social platforms shows that the company values their opinions and is committed to providing excellent customer service. This not only builds trust and loyalty but also strengthens the brand's reputation as a customer-centric organization.

Optimize social media marketing campaigns

When you know more about your audience and past content performance, you’re in a better position to create better posts that resonate. Learn what type of content your audience prefers based on engagement metrics. Tailor your content and messaging to reflect their interests and needs.

You’ll also have insights about what’s hot in the social media world. You can use these trends as the foundation for your own content, taking the guesswork out of what you should talk about.

Tip: Learn more about tailoring your content and messaging in our Personalization at Scale Guide !

image of a social media specialist checking her smartphone at her desk

Collecting social media research from outside data sources brings ethics and privacy into question. Marketers should be proactive in asking where their data is coming from and how it was obtained.

Ideally, you’ll choose tools that are in compliance with regulations like GDPR and CCPA. Know how they obtain data and whether they safeguard individual users’ information. Getting ahead of your competitors shouldn't be at the expense of your customers’ privacy or potential legal challenges.

Companies around the world use social media research to drive engagement, create better content, and grow their brand presence.

Take Shiseido , for instance. This Meltwater customer uses our Explore solution to learn what makes their brand special across 120 markets. The company uses social listening to monitor competitors, unify social mentions in a single dashboard, and understand the brand’s presence on a global stage.

Another Meltwater customer, Fifty Acres , uses the platform to learn about relevant narratives happening on social media. Learning what others are talking about allows them to shape their own stories, pitch new ideas for business growth, and connect with people in the right places at the right times.

W Hotels in Singapore is another great example of social media research at work. The company uses Meltwater to learn more about what customers like when traveling, allowing them to create custom experiences in their hotels.

Last but not least, Mailchimp uses Meltwater to inform its content strategy. The company looks for trends and themes on social media that resonate with creators, allowing them to easily scale their content by making their audience go bananas over every post.

Learn more when you request a demo by filling out the form below.

An overhead image of people crossing the street in a crosswalk, used for Meltwater's Personalization at Scale Guide.

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ORIGINAL RESEARCH article

Effects of social media use on psychological well-being: a mediated model.

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1 School of Finance and Economics, Jiangsu University, Zhenjiang, China
2 Research Unit of Governance, Competitiveness, and Public Policies (GOVCOPP), Center for Economics and Finance (cef.up), School of Economics and Management, University of Porto, Porto, Portugal
3 Department of Business Administration, Sukkur Institute of Business Administration (IBA) University, Sukkur, Pakistan
4 CETYS Universidad, Tijuana, Mexico
5 Department of Business Administration, Al-Quds University, Jerusalem, Israel
6 Business School, Shandong University, Weihai, China

The growth in social media use has given rise to concerns about the impacts it may have on users' psychological well-being. This paper's main objective is to shed light on the effect of social media use on psychological well-being. Building on contributions from various fields in the literature, it provides a more comprehensive study of the phenomenon by considering a set of mediators, including social capital types (i.e., bonding social capital and bridging social capital), social isolation, and smartphone addiction. The paper includes a quantitative study of 940 social media users from Mexico, using structural equation modeling (SEM) to test the proposed hypotheses. The findings point to an overall positive indirect impact of social media usage on psychological well-being, mainly due to the positive effect of bonding and bridging social capital. The empirical model's explanatory power is 45.1%. This paper provides empirical evidence and robust statistical analysis that demonstrates both positive and negative effects coexist, helping to reconcile the inconsistencies found so far in the literature.

Introduction

The use of social media has grown substantially in recent years ( Leong et al., 2019 ; Kemp, 2020 ). Social media refers to “the websites and online tools that facilitate interactions between users by providing them opportunities to share information, opinions, and interest” ( Swar and Hameed, 2017 , p. 141). Individuals use social media for many reasons, including entertainment, communication, and searching for information. Notably, adolescents and young adults are spending an increasing amount of time on online networking sites, e-games, texting, and other social media ( Twenge and Campbell, 2019 ). In fact, some authors (e.g., Dhir et al., 2018 ; Tateno et al., 2019 ) have suggested that social media has altered the forms of group interaction and its users' individual and collective behavior around the world.

Consequently, there are increased concerns regarding the possible negative impacts associated with social media usage addiction ( Swar and Hameed, 2017 ; Kircaburun et al., 2020 ), particularly on psychological well-being ( Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ). Smartphones sometimes distract their users from relationships and social interaction ( Chotpitayasunondh and Douglas, 2016 ; Li et al., 2020a ), and several authors have stressed that the excessive use of social media may lead to smartphone addiction ( Swar and Hameed, 2017 ; Leong et al., 2019 ), primarily because of the fear of missing out ( Reer et al., 2019 ; Roberts and David, 2020 ). Social media usage has been associated with anxiety, loneliness, and depression ( Dhir et al., 2018 ; Reer et al., 2019 ), social isolation ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ), and “phubbing,” which refers to the extent to which an individual uses, or is distracted by, their smartphone during face-to-face communication with others ( Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ).

However, social media use also contributes to building a sense of connectedness with relevant others ( Twenge and Campbell, 2019 ), which may reduce social isolation. Indeed, social media provides several ways to interact both with close ties, such as family, friends, and relatives, and weak ties, including coworkers, acquaintances, and strangers ( Chen and Li, 2017 ), and plays a key role among people of all ages as they exploit their sense of belonging in different communities ( Roberts and David, 2020 ). Consequently, despite the fears regarding the possible negative impacts of social media usage on well-being, there is also an increasing number of studies highlighting social media as a new communication channel ( Twenge and Campbell, 2019 ; Barbosa et al., 2020 ), stressing that it can play a crucial role in developing one's presence, identity, and reputation, thus facilitating social interaction, forming and maintaining relationships, and sharing ideas ( Carlson et al., 2016 ), which consequently may be significantly correlated to social support ( Chen and Li, 2017 ; Holliman et al., 2021 ). Interestingly, recent studies (e.g., David et al., 2018 ; Bano et al., 2019 ; Barbosa et al., 2020 ) have suggested that the impact of smartphone usage on psychological well-being depends on the time spent on each type of application and the activities that users engage in.

Hence, the literature provides contradictory cues regarding the impacts of social media on users' well-being, highlighting both the possible negative impacts and the social enhancement it can potentially provide. In line with views on the need to further investigate social media usage ( Karikari et al., 2017 ), particularly regarding its societal implications ( Jiao et al., 2017 ), this paper argues that there is an urgent need to further understand the impact of the time spent on social media on users' psychological well-being, namely by considering other variables that mediate and further explain this effect.

One of the relevant perspectives worth considering is that provided by social capital theory, which is adopted in this paper. Social capital theory has previously been used to study how social media usage affects psychological well-being (e.g., Bano et al., 2019 ). However, extant literature has so far presented only partial models of associations that, although statistically acceptable and contributing to the understanding of the scope of social networks, do not provide as comprehensive a vision of the phenomenon as that proposed within this paper. Furthermore, the contradictory views, suggesting both negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Van Den Eijnden et al., 2016 ; Jiao et al., 2017 ; Whaite et al., 2018 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) and positive impacts ( Carlson et al., 2016 ; Chen and Li, 2017 ; Twenge and Campbell, 2019 ) of social media on psychological well-being, have not been adequately explored.

Given this research gap, this paper's main objective is to shed light on the effect of social media use on psychological well-being. As explained in detail in the next section, this paper explores the mediating effect of bonding and bridging social capital. To provide a broad view of the phenomenon, it also considers several variables highlighted in the literature as affecting the relationship between social media usage and psychological well-being, namely smartphone addiction, social isolation, and phubbing. The paper utilizes a quantitative study conducted in Mexico, comprising 940 social media users, and uses structural equation modeling (SEM) to test a set of research hypotheses.

This article provides several contributions. First, it adds to existing literature regarding the effect of social media use on psychological well-being and explores the contradictory indications provided by different approaches. Second, it proposes a conceptual model that integrates complementary perspectives on the direct and indirect effects of social media use. Third, it offers empirical evidence and robust statistical analysis that demonstrates that both positive and negative effects coexist, helping resolve the inconsistencies found so far in the literature. Finally, this paper provides insights on how to help reduce the potential negative effects of social media use, as it demonstrates that, through bridging and bonding social capital, social media usage positively impacts psychological well-being. Overall, the article offers valuable insights for academics, practitioners, and society in general.

The remainder of this paper is organized as follows. Section Literature Review presents a literature review focusing on the factors that explain the impact of social media usage on psychological well-being. Based on the literature review, a set of hypotheses are defined, resulting in the proposed conceptual model, which includes both the direct and indirect effects of social media usage on psychological well-being. Section Research Methodology explains the methodological procedures of the research, followed by the presentation and discussion of the study's results in section Results. Section Discussion is dedicated to the conclusions and includes implications, limitations, and suggestions for future research.

Literature Review

Putnam (1995 , p. 664–665) defined social capital as “features of social life – networks, norms, and trust – that enable participants to act together more effectively to pursue shared objectives.” Li and Chen (2014 , p. 117) further explained that social capital encompasses “resources embedded in one's social network, which can be assessed and used for instrumental or expressive returns such as mutual support, reciprocity, and cooperation.”

Putnam (1995 , 2000) conceptualized social capital as comprising two dimensions, bridging and bonding, considering the different norms and networks in which they occur. Bridging social capital refers to the inclusive nature of social interaction and occurs when individuals from different origins establish connections through social networks. Hence, bridging social capital is typically provided by heterogeneous weak ties ( Li and Chen, 2014 ). This dimension widens individual social horizons and perspectives and provides extended access to resources and information. Bonding social capital refers to the social and emotional support each individual receives from his or her social networks, particularly from close ties (e.g., family and friends).

Overall, social capital is expected to be positively associated with psychological well-being ( Bano et al., 2019 ). Indeed, Williams (2006) stressed that interaction generates affective connections, resulting in positive impacts, such as emotional support. The following sub-sections use the lens of social capital theory to explore further the relationship between the use of social media and psychological well-being.

Social Media Use, Social Capital, and Psychological Well-Being

The effects of social media usage on social capital have gained increasing scholarly attention, and recent studies have highlighted a positive relationship between social media use and social capital ( Brown and Michinov, 2019 ; Tefertiller et al., 2020 ). Li and Chen (2014) hypothesized that the intensity of Facebook use by Chinese international students in the United States was positively related to social capital forms. A longitudinal survey based on the quota sampling approach illustrated the positive effects of social media use on the two social capital dimensions ( Chen and Li, 2017 ). Abbas and Mesch (2018) argued that, as Facebook usage increases, it will also increase users' social capital. Karikari et al. (2017) also found positive effects of social media use on social capital. Similarly, Pang (2018) studied Chinese students residing in Germany and found positive effects of social networking sites' use on social capital, which, in turn, was positively associated with psychological well-being. Bano et al. (2019) analyzed the 266 students' data and found positive effects of WhatsApp use on social capital forms and the positive effect of social capital on psychological well-being, emphasizing the role of social integration in mediating this positive effect.

Kim and Kim (2017) stressed the importance of having a heterogeneous network of contacts, which ultimately enhances the potential social capital. Overall, the manifest and social relations between people from close social circles (bonding social capital) and from distant social circles (bridging social capital) are strengthened when they promote communication, social support, and the sharing of interests, knowledge, and skills, which are shared with other members. This is linked to positive effects on interactions, such as acceptance, trust, and reciprocity, which are related to the individuals' health and psychological well-being ( Bekalu et al., 2019 ), including when social media helps to maintain social capital between social circles that exist outside of virtual communities ( Ellison et al., 2007 ).

Grounded on the above literature, this study proposes the following hypotheses:

H1a: Social media use is positively associated with bonding social capital.

H1b: Bonding social capital is positively associated with psychological well-being.

H2a: Social media use is positively associated with bridging social capital.

H2b: Bridging social capital is positively associated with psychological well-being.

Social Media Use, Social Isolation, and Psychological Well-Being

Social isolation is defined as “a deficit of personal relationships or being excluded from social networks” ( Choi and Noh, 2019 , p. 4). The state that occurs when an individual lacks true engagement with others, a sense of social belonging, and a satisfying relationship is related to increased mortality and morbidity ( Primack et al., 2017 ). Those who experience social isolation are deprived of social relationships and lack contact with others or involvement in social activities ( Schinka et al., 2012 ). Social media usage has been associated with anxiety, loneliness, and depression ( Dhir et al., 2018 ; Reer et al., 2019 ), and social isolation ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ). However, some recent studies have argued that social media use decreases social isolation ( Primack et al., 2017 ; Meshi et al., 2020 ). Indeed, the increased use of social media platforms such as Facebook, WhatsApp, Instagram, and Twitter, among others, may provide opportunities for decreasing social isolation. For instance, the improved interpersonal connectivity achieved via videos and images on social media helps users evidence intimacy, attenuating social isolation ( Whaite et al., 2018 ).

Chappell and Badger (1989) stated that social isolation leads to decreased psychological well-being, while Choi and Noh (2019) concluded that greater social isolation is linked to increased suicide risk. Schinka et al. (2012) further argued that, when individuals experience social isolation from siblings, friends, family, or society, their psychological well-being tends to decrease. Thus, based on the literature cited above, this study proposes the following hypotheses:

H3a: Social media use is significantly associated with social isolation.

H3b: Social isolation is negatively associated with psychological well-being.

Social Media Use, Smartphone Addiction, Phubbing, and Psychological Well-Being

Smartphone addiction refers to “an individuals' excessive use of a smartphone and its negative effects on his/her life as a result of his/her inability to control his behavior” ( Gökçearslan et al., 2018 , p. 48). Regardless of its form, smartphone addiction results in social, medical, and psychological harm to people by limiting their ability to make their own choices ( Chotpitayasunondh and Douglas, 2016 ). The rapid advancement of information and communication technologies has led to the concept of social media, e-games, and also to smartphone addiction ( Chatterjee, 2020 ). The excessive use of smartphones for social media use, entertainment (watching videos, listening to music), and playing e-games is more common amongst people addicted to smartphones ( Jeong et al., 2016 ). In fact, previous studies have evidenced the relationship between social use and smartphone addiction ( Salehan and Negahban, 2013 ; Jeong et al., 2016 ; Swar and Hameed, 2017 ). In line with this, the following hypotheses are proposed:

H4a: Social media use is positively associated with smartphone addiction.

H4b: Smartphone addiction is negatively associated with psychological well-being.

While smartphones are bringing individuals closer, they are also, to some extent, pulling people apart ( Tonacci et al., 2019 ). For instance, they can lead to individuals ignoring others with whom they have close ties or physical interactions; this situation normally occurs due to extreme smartphone use (i.e., at the dinner table, in meetings, at get-togethers and parties, and in other daily activities). This act of ignoring others is called phubbing and is considered a common phenomenon in communication activities ( Guazzini et al., 2019 ; Chatterjee, 2020 ). Phubbing is also referred to as an act of snubbing others ( Chatterjee, 2020 ). This term was initially used in May 2012 by an Australian advertising agency to describe the “growing phenomenon of individuals ignoring their families and friends who were called phubbee (a person who is a recipients of phubbing behavior) victim of phubber (a person who start phubbing her or his companion)” ( Chotpitayasunondh and Douglas, 2018 ). Smartphone addiction has been found to be a determinant of phubbing ( Kim et al., 2018 ). Other recent studies have also evidenced the association between smartphones and phubbing ( Chotpitayasunondh and Douglas, 2016 ; Guazzini et al., 2019 ; Tonacci et al., 2019 ; Chatterjee, 2020 ). Vallespín et al. (2017 ) argued that phubbing behavior has a negative influence on psychological well-being and satisfaction. Furthermore, smartphone addiction is considered responsible for the development of new technologies. It may also negatively influence individual's psychological proximity ( Chatterjee, 2020 ). Therefore, based on the above discussion and calls for the association between phubbing and psychological well-being to be further explored, this study proposes the following hypotheses:

H5: Smartphone addiction is positively associated with phubbing.

H6: Phubbing is negatively associated with psychological well-being.

Indirect Relationship Between Social Media Use and Psychological Well-Being

Beyond the direct hypotheses proposed above, this study investigates the indirect effects of social media use on psychological well-being mediated by social capital forms, social isolation, and phubbing. As described above, most prior studies have focused on the direct influence of social media use on social capital forms, social isolation, smartphone addiction, and phubbing, as well as the direct impact of social capital forms, social isolation, smartphone addiction, and phubbing on psychological well-being. Very few studies, however, have focused on and evidenced the mediating role of social capital forms, social isolation, smartphone addiction, and phubbing derived from social media use in improving psychological well-being ( Chen and Li, 2017 ; Pang, 2018 ; Bano et al., 2019 ; Choi and Noh, 2019 ). Moreover, little is known about smartphone addiction's mediating role between social media use and psychological well-being. Therefore, this study aims to fill this gap in the existing literature by investigating the mediation of social capital forms, social isolation, and smartphone addiction. Further, examining the mediating influence will contribute to a more comprehensive understanding of social media use on psychological well-being via the mediating associations of smartphone addiction and psychological factors. Therefore, based on the above, we propose the following hypotheses (the conceptual model is presented in Figure 1 ):

H7: (a) Bonding social capital; (b) bridging social capital; (c) social isolation; and (d) smartphone addiction mediate the relationship between social media use and psychological well-being.

Figure 1 . Conceptual model.

Research Methodology

Sample procedure and online survey.

This study randomly selected students from universities in Mexico. We chose University students for the following reasons. First, students are considered the most appropriate sample for e-commerce studies, particularly in the social media context ( Oghazi et al., 2018 ; Shi et al., 2018 ). Second, University students are considered to be frequent users and addicted to smartphones ( Mou et al., 2017 ; Stouthuysen et al., 2018 ). Third, this study ensured that respondents were experienced, well-educated, and possessed sufficient knowledge of the drawbacks of social media and the extreme use of smartphones. A total sample size of 940 University students was ultimately achieved from the 1,500 students contacted, using a convenience random sampling approach, due both to the COVID-19 pandemic and budget and time constraints. Additionally, in order to test the model, a quantitative empirical study was conducted, using an online survey method to collect data. This study used a web-based survey distributed via social media platforms for two reasons: the COVID-19 pandemic; and to reach a large number of respondents ( Qalati et al., 2021 ). Furthermore, online surveys are considered a powerful and authenticated tool for new research ( Fan et al., 2021 ), while also representing a fast, simple, and less costly approach to collecting data ( Dutot and Bergeron, 2016 ).

Data Collection Procedures and Respondent's Information

Data were collected by disseminating a link to the survey by e-mail and social network sites. Before presenting the closed-ended questionnaire, respondents were assured that their participation would remain voluntary, confidential, and anonymous. Data collection occurred from July 2020 to December 2020 (during the pandemic). It should be noted that, because data were collected during the pandemic, this may have had an influence on the results of the study. The reason for choosing a six-month lag time was to mitigate common method bias (CMB) ( Li et al., 2020b ). In the present study, 1,500 students were contacted via University e-mail and social applications (Facebook, WhatsApp, and Instagram). We sent a reminder every month for 6 months (a total of six reminders), resulting in 940 valid responses. Thus, 940 (62.6% response rate) responses were used for hypotheses testing.

Table 1 reveals that, of the 940 participants, three-quarters were female (76.4%, n = 719) and nearly one-quarter (23.6%, n = 221) were male. Nearly half of the participants (48.8%, n = 459) were aged between 26 and 35 years, followed by 36 to 35 years (21.9%, n = 206), <26 (20.3%, n = 191), and over 45 (8.9%, n = 84). Approximately two-thirds (65%, n = 611) had a bachelor's degree or above, while one-third had up to 12 years of education. Regarding the daily frequency of using the Internet, nearly half (48.6%, n = 457) of the respondents reported between 5 and 8 h a day, and over one-quarter (27.2%) 9–12 h a day. Regarding the social media platforms used, over 38.5 and 39.6% reported Facebook and WhatsApp, respectively. Of the 940 respondents, only 22.1% reported Instagram (12.8%) and Twitter (9.2%). It should be noted, however, that the sample is predominantly female and well-educated.

Table 1 . Respondents' characteristics.

Measurement Items

The study used five-point Likert scales (1 = “strongly disagree;” 5 = “strongly agree”) to record responses.

Social Media Use

Social media use was assessed using four items adapted from Karikari et al. (2017) . Sample items include “Social media is part of my everyday activity,” “Social media has become part of my daily life,” “I would be sorry if social media shut down,” and “I feel out of touch, when I have not logged onto social media for a while.” The adapted items had robust reliability and validity (CA = 783, CR = 0.857, AVE = 0.600).

Social Capital

Social capital was measured using a total of eight items, representing bonding social capital (four items) and bridging social capital (four items) adapted from Chan (2015) . Sample construct items include: bonging social capital (“I am willing to spend time to support general community activities,” “I interact with people who are quite different from me”) and bridging social capital (“My social media community is a good place to be,” “Interacting with people on social media makes me want to try new things”). The adapted items had robust reliability and validity [bonding social capital (CA = 0.785, CR = 0.861, AVE = 0.608) and bridging social capital (CA = 0.834, CR = 0.883, AVE = 0.601)].

Social Isolation

Social isolation was assessed using three items from Choi and Noh (2019) . Sample items include “I do not have anyone to play with,” “I feel alone from people,” and “I have no one I can trust.” This adapted scale had substantial reliability and validity (CA = 0.890, CR = 0.928, AVE = 0.811).

Smartphone Addiction

Smartphone addiction was assessed using five items taken from Salehan and Negahban (2013) . Sample items include “I am always preoccupied with my mobile,” “Using my mobile phone keeps me relaxed,” and “I am not able to control myself from frequent use of mobile phones.” Again, these adapted items showed substantial reliability and validity (CA = 903, CR = 0.928, AVE = 0.809).

Phubbing was assessed using four items from Chotpitayasunondh and Douglas (2018) . Sample items include: “I have conflicts with others because I am using my phone” and “I would rather pay attention to my phone than talk to others.” This construct also demonstrated significant reliability and validity (CA = 770, CR = 0.894, AVE = 0.809).

Psychological Well-Being

Psychological well-being was assessed using five items from Jiao et al. (2017) . Sample items include “I lead a purposeful and meaningful life with the help of others,” “My social relationships are supportive and rewarding in social media,” and “I am engaged and interested in my daily on social media.” This study evidenced that this adapted scale had substantial reliability and validity (CA = 0.886, CR = 0.917, AVE = 0.688).

Data Analysis

Based on the complexity of the association between the proposed construct and the widespread use and acceptance of SmartPLS 3.0 in several fields ( Hair et al., 2019 ), we utilized SEM, using SmartPLS 3.0, to examine the relationships between constructs. Structural equation modeling is a multivariate statistical analysis technique that is used to investigate relationships. Further, it is a combination of factor and multivariate regression analysis, and is employed to explore the relationship between observed and latent constructs.

SmartPLS 3.0 “is a more comprehensive software program with an intuitive graphical user interface to run partial least square SEM analysis, certainly has had a massive impact” ( Sarstedt and Cheah, 2019 ). According to Ringle et al. (2015) , this commercial software offers a wide range of algorithmic and modeling options, improved usability, and user-friendly and professional support. Furthermore, Sarstedt and Cheah (2019) suggested that structural equation models enable the specification of complex interrelationships between observed and latent constructs. Hair et al. (2019) argued that, in recent years, the number of articles published using partial least squares SEM has increased significantly in contrast to covariance-based SEM. In addition, partial least squares SEM using SmartPLS is more appealing for several scholars as it enables them to predict more complex models with several variables, indicator constructs, and structural paths, instead of imposing distributional assumptions on the data ( Hair et al., 2019 ). Therefore, this study utilized the partial least squares SEM approach using SmartPLS 3.0.

Common Method Bias (CMB) Test

This study used the Kaiser–Meyer–Olkin (KMO) test to measure the sampling adequacy and ensure data suitability. The KMO test result was 0.874, which is greater than an acceptable threshold of 0.50 ( Ali Qalati et al., 2021 ; Shrestha, 2021 ), and hence considered suitable for explanatory factor analysis. Moreover, Bartlett's test results demonstrated a significance level of 0.001, which is considered good as it is below the accepted threshold of 0.05.

The term CMB is associated with Campbell and Fiske (1959) , who highlighted the importance of CMB and identified that a portion of variance in the research may be due to the methods employed. It occurs when all scales of the study are measured at the same time using a single questionnaire survey ( Podsakoff and Organ, 1986 ); subsequently, estimates of the relationship among the variables might be distorted by the impacts of CMB. It is considered a serious issue that has a potential to “jeopardize” the validity of the study findings ( Tehseen et al., 2017 ). There are several reasons for CMB: (1) it mainly occurs due to response “tendencies that raters can apply uniformity across the measures;” and (2) it also occurs due to similarities in the wording and structure of the survey items that produce similar results ( Jordan and Troth, 2019 ). Harman's single factor test and a full collinearity approach were employed to ensure that the data was free from CMB ( Tehseen et al., 2017 ; Jordan and Troth, 2019 ; Ali Qalati et al., 2021 ). Harman's single factor test showed a single factor explained only 22.8% of the total variance, which is far below the 50.0% acceptable threshold ( Podsakoff et al., 2003 ).

Additionally, the variance inflation factor (VIF) was used, which is a measure of the amount of multicollinearity in a set of multiple regression constructs and also considered a way of detecting CMB ( Hair et al., 2019 ). Hair et al. (2019) suggested that the acceptable threshold for the VIF is 3.0; as the computed VIFs for the present study ranged from 1.189 to 1.626, CMB is not a key concern (see Table 2 ). Bagozzi et al. (1991) suggested a correlation-matrix procedure to detect CMB. Common method bias is evident if correlation among the principle constructs is >0.9 ( Tehseen et al., 2020 ); however, no values >0.9 were found in this study (see section Assessment of Measurement Model). This study used a two-step approach to evaluate the measurement model and the structural model.

Table 2 . Common method bias (full collinearity VIF).

Assessment of Measurement Model

Before conducting the SEM analysis, the measurement model was assessed to examine individual item reliability, internal consistency, and convergent and discriminant validity. Table 3 exhibits the values of outer loading used to measure an individual item's reliability ( Hair et al., 2012 ). Hair et al. (2017) proposed that the value for each outer loading should be ≥0.7; following this principle, two items of phubbing (PHUB3—I get irritated if others ask me to get off my phone and talk to them; PHUB4—I use my phone even though I know it irritated others) were removed from the analysis Hair et al. (2019) . According to Nunnally (1978) , Cronbach's alpha values should exceed 0.7. The threshold values of constructs in this study ranged from 0.77 to 0.903. Regarding internal consistency, Bagozzi and Yi (1988) suggested that composite reliability (CR) should be ≥0.7. The coefficient value for CR in this study was between 0.857 and 0.928. Regarding convergent validity, Fornell and Larcker (1981) suggested that the average variance extracted (AVE) should be ≥0.5. Average variance extracted values in this study were between 0.60 and 0.811. Finally, regarding discriminant validity, according to Fornell and Larcker (1981) , the square root of the AVE for each construct should exceed the inter-correlations of the construct with other model constructs. That was the case in this study, as shown in Table 4 .

Table 3 . Study measures, factor loading, and the constructs' reliability and convergent validity.

Table 4 . Discriminant validity and correlation.

Hence, by analyzing the results of the measurement model, it can be concluded that the data are adequate for structural equation estimation.

Assessment of the Structural Model

This study used the PLS algorithm and a bootstrapping technique with 5,000 bootstraps as proposed by Hair et al. (2019) to generate the path coefficient values and their level of significance. The coefficient of determination ( R 2 ) is an important measure to assess the structural model and its explanatory power ( Henseler et al., 2009 ; Hair et al., 2019 ). Table 5 and Figure 2 reveal that the R 2 value in the present study was 0.451 for psychological well-being, which means that 45.1% of changes in psychological well-being occurred due to social media use, social capital forms (i.e., bonding and bridging), social isolation, smartphone addiction, and phubbing. Cohen (1998) proposed that R 2 values of 0.60, 0.33, and 0.19 are considered substantial, moderate, and weak. Following Cohen's (1998) threshold values, this research demonstrates a moderate predicting power for psychological well-being among Mexican respondents ( Table 6 ).

Table 5 . Summary of path coefficients and hypothesis testing.

Figure 2 . Structural model.

Table 6 . Strength of the model (Predictive relevance, coefficient of determination, and model fit indices).

Apart from the R 2 measure, the present study also used cross-validated redundancy measures, or effect sizes ( q 2 ), to assess the proposed model and validate the results ( Ringle et al., 2012 ). Hair et al. (2019) suggested that a model exhibiting an effect size q 2 > 0 has predictive relevance ( Table 6 ). This study's results evidenced that it has a 0.15 <0.29 <0.35 (medium) predictive relevance, as 0.02, 0.15, and 0.35 are considered small, medium, and large, respectively ( Cohen, 1998 ). Regarding the goodness-of-fit indices, Hair et al. (2019) suggested the standardized root mean square residual (SRMR) to evaluate the goodness of fit. Standardized root mean square is an absolute measure of fit: a value of zero indicates perfect fit and a value <0.08 is considered good fit ( Hair et al., 2019 ). This study exhibits an adequate model fitness level with an SRMR value of 0.063 ( Table 6 ).

Table 5 reveals that all hypotheses of the study were accepted base on the criterion ( p -value < 0.05). H1a (β = 0.332, t = 10.283, p = 0.001) was confirmed, with the second most robust positive and significant relationship (between social media use and bonding social capital). In addition, this study evidenced a positive and significant relationship between bonding social capital and psychological well-being (β = 0.127, t = 4.077, p = 0.001); therefore, H1b was accepted. Regarding social media use and bridging social capital, the present study found the most robust positive and significant impact (β = 0.439, t = 15.543, p = 0.001); therefore, H2a was accepted. The study also evidenced a positive and significant association between bridging social capital and psychological well-being (β = 0.561, t = 20.953, p = 0.001); thus, H2b was accepted. The present study evidenced a significant effect of social media use on social isolation (β = 0.145, t = 4.985, p = 0.001); thus, H3a was accepted. In addition, this study accepted H3b (β = −0.051, t = 2.01, p = 0.044). Furthermore, this study evidenced a positive and significant effect of social media use on smartphone addiction (β = 0.223, t = 6.241, p = 0.001); therefore, H4a was accepted. Furthermore, the present study found that smartphone addiction has a negative significant influence on psychological well-being (β = −0.068, t = 2.387, p = 0.017); therefore, H4b was accepted. Regarding the relationship between smartphone addiction and phubbing, this study found a positive and significant effect of smartphone addiction on phubbing (β = 0.244, t = 7.555, p = 0.001); therefore, H5 was accepted. Furthermore, the present research evidenced a positive and significant influence of phubbing on psychological well-being (β = 0.137, t = 4.938, p = 0.001); therefore, H6 was accepted. Finally, the study provides interesting findings on the indirect effect of social media use on psychological well-being ( t -value > 1.96 and p -value < 0.05); therefore, H7a–d were accepted.

Furthermore, to test the mediating analysis, Preacher and Hayes's (2008) approach was used. The key characteristic of an indirect relationship is that it involves a third construct, which plays a mediating role in the relationship between the independent and dependent constructs. Logically, the effect of A (independent construct) on C (the dependent construct) is mediated by B (a third variable). Preacher and Hayes (2008) suggested the following: B is a construct acting as a mediator if A significantly influences B, A significantly accounts for variability in C, B significantly influences C when controlling for A, and the influence of A on C decreases significantly when B is added simultaneously with A as a predictor of C. According to Matthews et al. (2018) , if the indirect effect is significant while the direct insignificant, full mediation has occurred, while if both direct and indirect effects are substantial, partial mediation has occurred. This study evidenced that there is partial mediation in the proposed construct ( Table 5 ). Following Preacher and Hayes (2008) this study evidenced that there is partial mediation in the proposed construct, because the relationship between independent variable (social media use) and dependent variable (psychological well-being) is significant ( p -value < 0.05) and indirect effect among them after introducing mediator (bonding social capital, bridging social capital, social isolation, and smartphone addiction) is also significant ( p -value < 0.05), therefore it is evidenced that when there is a significant effect both direct and indirect it's called partial mediation.

The present study reveals that the social and psychological impacts of social media use among University students is becoming more complex as there is continuing advancement in technology, offering a range of affordable interaction opportunities. Based on the 940 valid responses collected, all the hypotheses were accepted ( p < 0.05).

H1a finding suggests that social media use is a significant influencing factor of bonding social capital. This implies that, during a pandemic, social media use enables students to continue their close relationships with family members, friends, and those with whom they have close ties. This finding is in line with prior work of Chan (2015) and Ellison et al. (2007) , who evidenced that social bonding capital is predicted by Facebook use and having a mobile phone. H1b findings suggest that, when individuals believe that social communication can help overcome obstacles to interaction and encourage more virtual self-disclosure, social media use can improve trust and promote the establishment of social associations, thereby enhancing well-being. These findings are in line with those of Gong et al. (2021) , who also witnessed the significant effect of bonding social capital on immigrants' psychological well-being, subsequently calling for the further evidence to confirm the proposed relationship.

The findings of the present study related to H2a suggest that students are more likely to use social media platforms to receive more emotional support, increase their ability to mobilize others, and to build social networks, which leads to social belongingness. Furthermore, the findings suggest that social media platforms enable students to accumulate and maintain bridging social capital; further, online classes can benefit students who feel shy when participating in offline classes. This study supports the previous findings of Chan (2015) and Karikari et al. (2017) . Notably, the present study is not limited to a single social networking platform, taking instead a holistic view of social media. The H2b findings are consistent with those of Bano et al. (2019) , who also confirmed the link between bonding social capital and psychological well-being among University students using WhatsApp as social media platform, as well as those of Chen and Li (2017) .

The H3a findings suggest that, during the COVID-19 pandemic when most people around the world have had limited offline or face-to-face interaction and have used social media to connect with families, friends, and social communities, they have often been unable to connect with them. This is due to many individuals avoiding using social media because of fake news, financial constraints, and a lack of trust in social media; thus, the lack both of offline and online interaction, coupled with negative experiences on social media use, enhances the level of social isolation ( Hajek and König, 2021 ). These findings are consistent with those of Adnan and Anwar (2020) . The H3b suggests that higher levels of social isolation have a negative impact on psychological well-being. These result indicating that, consistent with Choi and Noh (2019) , social isolation is negatively and significantly related to psychological well-being.

The H4a results suggests that substantial use of social media use leads to an increase in smartphone addiction. These findings are in line with those of Jeong et al. (2016) , who stated that the excessive use of smartphones for social media, entertainment (watching videos, listening to music), and playing e-games was more likely to lead to smartphone addiction. These findings also confirm the previous work of Jeong et al. (2016) , Salehan and Negahban (2013) , and Swar and Hameed (2017) . The H4b results revealed that a single unit increase in smartphone addiction results in a 6.8% decrease in psychological well-being. These findings are in line with those of Tangmunkongvorakul et al. (2019) , who showed that students with higher levels of smartphone addiction had lower psychological well-being scores. These findings also support those of Shoukat (2019) , who showed that smartphone addiction inversely influences individuals' mental health.

This suggests that the greater the smartphone addiction, the greater the phubbing. The H5 findings are in line with those of Chatterjee (2020) , Chotpitayasunondh and Douglas (2016) , Guazzini et al. (2019) , and Tonacci et al. (2019) , who also evidenced a significant impact of smartphone addiction and phubbing. Similarly, Chotpitayasunondh and Douglas (2018) corroborated that smartphone addiction is the main predictor of phubbing behavior. However, these findings are inconsistent with those of Vallespín et al. (2017 ), who found a negative influence of phubbing.

The H6 results suggests that phubbing is one of the significant predictors of psychological well-being. Furthermore, these findings suggest that, when phubbers use a cellphone during interaction with someone, especially during the current pandemic, and they are connected with many family members, friends, and relatives; therefore, this kind of action gives them more satisfaction, which simultaneously results in increased relaxation and decreased depression ( Chotpitayasunondh and Douglas, 2018 ). These findings support those of Davey et al. (2018) , who evidenced that phubbing has a significant influence on adolescents and social health students in India.

The findings showed a significant and positive effect of social media use on psychological well-being both through bridging and bonding social capital. However, a significant and negative effect of social media use on psychological well-being through smartphone addiction and through social isolation was also found. Hence, this study provides evidence that could shed light on the contradictory contributions in the literature suggesting both positive (e.g., Chen and Li, 2017 ; Twenge and Campbell, 2019 ; Roberts and David, 2020 ) and negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) effects of social media use on psychological well-being. This study concludes that the overall impact is positive, despite some degree of negative indirect impact.

Theoretical Contributions

This study's findings contribute to the current literature, both by providing empirical evidence for the relationships suggested by extant literature and by demonstrating the relevance of adopting a more complex approach that considers, in particular, the indirect effect of social media on psychological well-being. As such, this study constitutes a basis for future research ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ) aiming to understand the impacts of social media use and to find ways to reduce its possible negative impacts.

In line with Kim and Kim (2017) , who stressed the importance of heterogeneous social networks in improving social capital, this paper suggests that, to positively impact psychological well-being, social media usage should be associated both with strong and weak ties, as both are important in building social capital, and hence associated with its bonding and bridging facets. Interestingly, though, bridging capital was shown as having the greatest impact on psychological well-being. Thus, the importance of wider social horizons, the inclusion in different groups, and establishing new connections ( Putnam, 1995 , 2000 ) with heterogeneous weak ties ( Li and Chen, 2014 ) are highlighted in this paper.

Practical Contributions

These findings are significant for practitioners, particularly those interested in dealing with the possible negative impacts of social media use on psychological well-being. Although social media use is associated with factors that negatively impact psychological well-being, particularly smartphone addiction and social isolation, these negative impacts can be lessened if the connections with both strong and weak ties are facilitated and featured by social media. Indeed, social media platforms offer several features, from facilitating communication with family, friends, and acquaintances, to identifying and offering access to other people with shared interests. However, it is important to access heterogeneous weak ties ( Li and Chen, 2014 ) so that social media offers access to wider sources of information and new resources, hence enhancing bridging social capital.

Limitations and Directions for Future Studies

This study is not without limitations. For example, this study used a convenience sampling approach to reach to a large number of respondents. Further, this study was conducted in Mexico only, limiting the generalizability of the results; future research should therefore use a cross-cultural approach to investigate the impacts of social media use on psychological well-being and the mediating role of proposed constructs (e.g., bonding and bridging social capital, social isolation, and smartphone addiction). The sample distribution may also be regarded as a limitation of the study because respondents were mainly well-educated and female. Moreover, although Internet channels represent a particularly suitable way to approach social media users, the fact that this study adopted an online survey does not guarantee a representative sample of the population. Hence, extrapolating the results requires caution, and study replication is recommended, particularly with social media users from other countries and cultures. The present study was conducted in the context of mainly University students, primarily well-educated females, via an online survey on in Mexico; therefore, the findings represent a snapshot at a particular time. Notably, however, the effect of social media use is increasing due to COVID-19 around the globe and is volatile over time.

Two of the proposed hypotheses of this study, namely the expected negative impacts of social media use on social isolation and of phubbing on psychological well-being, should be further explored. One possible approach is to consider the type of connections (i.e., weak and strong ties) to explain further the impact of social media usage on social isolation. Apparently, the prevalence of weak ties, although facilitating bridging social capital, may have an adverse impact in terms of social isolation. Regarding phubbing, the fact that the findings point to a possible positive impact on psychological well-being should be carefully addressed, specifically by psychology theorists and scholars, in order to identify factors that may help further understand this phenomenon. Other suggestions for future research include using mixed-method approaches, as qualitative studies could help further validate the results and provide complementary perspectives on the relationships between the considered variables.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Jiangsu University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

This study is supported by the National Statistics Research Project of China (2016LY96).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbas, R., and Mesch, G. (2018). Do rich teens get richer? Facebook use and the link between offline and online social capital among Palestinian youth in Israel. Inf. Commun. Soc. 21, 63–79. doi: 10.1080/1369118X.2016.1261168

CrossRef Full Text | Google Scholar

Adnan, M., and Anwar, K. (2020). Online learning amid the COVID-19 pandemic: students' perspectives. J. Pedagog. Sociol. Psychol. 2, 45–51. doi: 10.33902/JPSP.2020261309

PubMed Abstract | CrossRef Full Text | Google Scholar

Ali Qalati, S., Li, W., Ahmed, N., Ali Mirani, M., and Khan, A. (2021). Examining the factors affecting SME performance: the mediating role of social media adoption. Sustainability 13:75. doi: 10.3390/su13010075

Bagozzi, R. P., and Yi, Y. (1988). On the evaluation of structural equation models. J. Acad. Mark. Sci. 16, 74–94. doi: 10.1007/BF02723327

Bagozzi, R. P., Yi, Y., and Phillips, L. W. (1991). Assessing construct validity in organizational research. Admin. Sci. Q. 36, 421–458. doi: 10.2307/2393203

Bano, S., Cisheng, W., Khan, A. N., and Khan, N. A. (2019). WhatsApp use and student's psychological well-being: role of social capital and social integration. Child. Youth Serv. Rev. 103, 200–208. doi: 10.1016/j.childyouth.2019.06.002

Barbosa, B., Chkoniya, V., Simoes, D., Filipe, S., and Santos, C. A. (2020). Always connected: generation Y smartphone use and social capital. Rev. Ibérica Sist. Tecnol. Inf. E 35, 152–166.

Google Scholar

Bekalu, M. A., McCloud, R. F., and Viswanath, K. (2019). Association of social media use with social well-being, positive mental health, and self-rated health: disentangling routine use from emotional connection to use. Health Educ. Behav. 46(2 Suppl), 69S−80S. doi: 10.1177/1090198119863768

Brown, G., and Michinov, N. (2019). Measuring latent ties on Facebook: a novel approach to studying their prevalence and relationship with bridging social capital. Technol. Soc. 59:101176. doi: 10.1016/j.techsoc.2019.101176

Campbell, D. T., and Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol. Bull. 56, 81–105. doi: 10.1037/h0046016

Carlson, J. R., Zivnuska, S., Harris, R. B., Harris, K. J., and Carlson, D. S. (2016). Social media use in the workplace: a study of dual effects. J. Org. End User Comput. 28, 15–31. doi: 10.4018/JOEUC.2016010102

Chan, M. (2015). Mobile phones and the good life: examining the relationships among mobile use, social capital and subjective well-being. New Media Soc. 17, 96–113. doi: 10.1177/1461444813516836

Chappell, N. L., and Badger, M. (1989). Social isolation and well-being. J. Gerontol. 44, S169–S176. doi: 10.1093/geronj/44.5.s169

Chatterjee, S. (2020). Antecedents of phubbing: from technological and psychological perspectives. J. Syst. Inf. Technol. 22, 161–118. doi: 10.1108/JSIT-05-2019-0089

Chen, H.-T., and Li, X. (2017). The contribution of mobile social media to social capital and psychological well-being: examining the role of communicative use, friending and self-disclosure. Comput. Hum. Behav. 75, 958–965. doi: 10.1016/j.chb.2017.06.011

Choi, D.-H., and Noh, G.-Y. (2019). The influence of social media use on attitude toward suicide through psychological well-being, social isolation, and social support. Inf. Commun. Soc. 23, 1–17. doi: 10.1080/1369118X.2019.1574860

Chotpitayasunondh, V., and Douglas, K. M. (2016). How “phubbing” becomes the norm: the antecedents and consequences of snubbing via smartphone. Comput. Hum. Behav. 63, 9–18. doi: 10.1016/j.chb.2016.05.018

Chotpitayasunondh, V., and Douglas, K. M. (2018). The effects of “phubbing” on social interaction. J. Appl. Soc. Psychol. 48, 304–316. doi: 10.1111/jasp.12506

Cohen, J. (1998). Statistical Power Analysis for the Behavioural Sciences . Hillsdale, NJ: Lawrence Erlbaum Associates.

Davey, S., Davey, A., Raghav, S. K., Singh, J. V., Singh, N., Blachnio, A., et al. (2018). Predictors and consequences of “phubbing” among adolescents and youth in India: an impact evaluation study. J. Fam. Community Med. 25, 35–42. doi: 10.4103/jfcm.JFCM_71_17

David, M. E., Roberts, J. A., and Christenson, B. (2018). Too much of a good thing: investigating the association between actual smartphone use and individual well-being. Int. J. Hum. Comput. Interact. 34, 265–275. doi: 10.1080/10447318.2017.1349250

Dhir, A., Yossatorn, Y., Kaur, P., and Chen, S. (2018). Online social media fatigue and psychological wellbeing—a study of compulsive use, fear of missing out, fatigue, anxiety and depression. Int. J. Inf. Manag. 40, 141–152. doi: 10.1016/j.ijinfomgt.2018.01.012

Dutot, V., and Bergeron, F. (2016). From strategic orientation to social media orientation: improving SMEs' performance on social media. J. Small Bus. Enterp. Dev. 23, 1165–1190. doi: 10.1108/JSBED-11-2015-0160

Ellison, N. B., Steinfield, C., and Lampe, C. (2007). The benefits of Facebook “friends:” Social capital and college students' use of online social network sites. J. Comput. Mediat. Commun. 12, 1143–1168. doi: 10.1111/j.1083-6101.2007.00367.x

Fan, M., Huang, Y., Qalati, S. A., Shah, S. M. M., Ostic, D., and Pu, Z. (2021). Effects of information overload, communication overload, and inequality on digital distrust: a cyber-violence behavior mechanism. Front. Psychol. 12:643981. doi: 10.3389/fpsyg.2021.643981

Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. J. Market. Res. 18, 39–50. doi: 10.1177/002224378101800104

Gökçearslan, S., Uluyol, Ç., and Sahin, S. (2018). Smartphone addiction, cyberloafing, stress and social support among University students: a path analysis. Child. Youth Serv. Rev. 91, 47–54. doi: 10.1016/j.childyouth.2018.05.036

Gong, S., Xu, P., and Wang, S. (2021). Social capital and psychological well-being of Chinese immigrants in Japan. Int. J. Environ. Res. Public Health 18:547. doi: 10.3390/ijerph18020547

Guazzini, A., Duradoni, M., Capelli, A., and Meringolo, P. (2019). An explorative model to assess individuals' phubbing risk. Fut. Internet 11:21. doi: 10.3390/fi11010021

Hair, J. F., Risher, J. J., Sarstedt, M., and Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 31, 2–24. doi: 10.1108/EBR-11-2018-0203

Hair, J. F., Sarstedt, M., Pieper, T. M., and Ringle, C. M. (2012). The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long Range Plann. 45, 320–340. doi: 10.1016/j.lrp.2012.09.008

Hair, J. F., Sarstedt, M., Ringle, C. M., and Gudergan, S. P. (2017). Advanced Issues in Partial Least Squares Structural Equation Modeling. Thousand Oaks, CA: Sage.

Hajek, A., and König, H.-H. (2021). Social isolation and loneliness of older adults in times of the CoViD-19 pandemic: can use of online social media sites and video chats assist in mitigating social isolation and loneliness? Gerontology 67, 121–123. doi: 10.1159/000512793

Henseler, J., Ringle, C. M., and Sinkovics, R. R. (2009). “The use of partial least squares path modeling in international marketing,” in New Challenges to International Marketing , Vol. 20, eds R.R. Sinkovics and P.N. Ghauri (Bigley: Emerald), 277–319.

Holliman, A. J., Waldeck, D., Jay, B., Murphy, S., Atkinson, E., Collie, R. J., et al. (2021). Adaptability and social support: examining links with psychological wellbeing among UK students and non-students. Fron. Psychol. 12:636520. doi: 10.3389/fpsyg.2021.636520

Jeong, S.-H., Kim, H., Yum, J.-Y., and Hwang, Y. (2016). What type of content are smartphone users addicted to? SNS vs. games. Comput. Hum. Behav. 54, 10–17. doi: 10.1016/j.chb.2015.07.035

Jiao, Y., Jo, M.-S., and Sarigöllü, E. (2017). Social value and content value in social media: two paths to psychological well-being. J. Org. Comput. Electr. Commer. 27, 3–24. doi: 10.1080/10919392.2016.1264762

Jordan, P. J., and Troth, A. C. (2019). Common method bias in applied settings: the dilemma of researching in organizations. Austr. J. Manag. 45, 3–14. doi: 10.1177/0312896219871976

Karikari, S., Osei-Frimpong, K., and Owusu-Frimpong, N. (2017). Evaluating individual level antecedents and consequences of social media use in Ghana. Technol. Forecast. Soc. Change 123, 68–79. doi: 10.1016/j.techfore.2017.06.023

Kemp, S. (January 30, 2020). Digital 2020: 3.8 billion people use social media. We Are Social . Available online at: https://wearesocial.com/blog/2020/01/digital-2020-3-8-billion-people-use-social-media .

Kim, B., and Kim, Y. (2017). College students' social media use and communication network heterogeneity: implications for social capital and subjective well-being. Comput. Hum. Behav. 73, 620–628. doi: 10.1016/j.chb.2017.03.033

Kim, K., Milne, G. R., and Bahl, S. (2018). Smart phone addiction and mindfulness: an intergenerational comparison. Int. J. Pharmaceut. Healthcare Market. 12, 25–43. doi: 10.1108/IJPHM-08-2016-0044

Kircaburun, K., Alhabash, S., Tosuntaş, S. B., and Griffiths, M. D. (2020). Uses and gratifications of problematic social media use among University students: a simultaneous examination of the big five of personality traits, social media platforms, and social media use motives. Int. J. Mental Health Addict. 18, 525–547. doi: 10.1007/s11469-018-9940-6

Leong, L.-Y., Hew, T.-S., Ooi, K.-B., Lee, V.-H., and Hew, J.-J. (2019). A hybrid SEM-neural network analysis of social media addiction. Expert Syst. Appl. 133, 296–316. doi: 10.1016/j.eswa.2019.05.024

Li, L., Griffiths, M. D., Mei, S., and Niu, Z. (2020a). Fear of missing out and smartphone addiction mediates the relationship between positive and negative affect and sleep quality among Chinese University students. Front. Psychiatr. 11:877. doi: 10.3389/fpsyt.2020.00877

Li, W., Qalati, S. A., Khan, M. A. S., Kwabena, G. Y., Erusalkina, D., and Anwar, F. (2020b). Value co-creation and growth of social enterprises in developing countries: moderating role of environmental dynamics. Entrep. Res. J. 2020:20190359. doi: 10.1515/erj-2019-0359

Li, X., and Chen, W. (2014). Facebook or Renren? A comparative study of social networking site use and social capital among Chinese international students in the United States. Comput. Hum. Behav . 35, 116–123. doi: 10.1016/j.chb.2014.02.012

Matthews, L., Hair, J. F., and Matthews, R. (2018). PLS-SEM: the holy grail for advanced analysis. Mark. Manag. J. 28, 1–13.

Meshi, D., Cotten, S. R., and Bender, A. R. (2020). Problematic social media use and perceived social isolation in older adults: a cross-sectional study. Gerontology 66, 160–168. doi: 10.1159/000502577

Mou, J., Shin, D.-H., and Cohen, J. (2017). Understanding trust and perceived usefulness in the consumer acceptance of an e-service: a longitudinal investigation. Behav. Inf. Technol. 36, 125–139. doi: 10.1080/0144929X.2016.1203024

Nunnally, J. (1978). Psychometric Methods . New York, NY: McGraw-Hill.

Oghazi, P., Karlsson, S., Hellström, D., and Hjort, K. (2018). Online purchase return policy leniency and purchase decision: mediating role of consumer trust. J. Retail. Consumer Serv. 41, 190–200.

Pang, H. (2018). Exploring the beneficial effects of social networking site use on Chinese students' perceptions of social capital and psychological well-being in Germany. Int. J. Intercult. Relat. 67, 1–11. doi: 10.1016/j.ijintrel.2018.08.002

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., and Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88, 879–903. doi: 10.1037/0021-9010.88.5.879

Podsakoff, P. M., and Organ, D. W. (1986). Self-reports in organizational research: problems and prospects. J. Manag. 12, 531–544. doi: 10.1177/014920638601200408

Preacher, K. J., and Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res. Methods 40, 879–891. doi: 10.3758/brm.40.3.879

Primack, B. A., Shensa, A., Sidani, J. E., Whaite, E. O., yi Lin, L., Rosen, D., et al. (2017). Social media use and perceived social isolation among young adults in the US. Am. J. Prev. Med. 53, 1–8. doi: 10.1016/j.amepre.2017.01.010

Putnam, R. D. (1995). Tuning in, tuning out: the strange disappearance of social capital in America. Polit. Sci. Polit. 28, 664–684. doi: 10.2307/420517

Putnam, R. D. (2000). Bowling Alone: The Collapse and Revival of American Community . New York, NY: Simon and Schuster.

Qalati, S. A., Ostic, D., Fan, M., Dakhan, S. A., Vela, E. G., Zufar, Z., et al. (2021). The general public knowledge, attitude, and practices regarding COVID-19 during the lockdown in Asian developing countries. Int. Q. Commun. Health Educ. 2021:272684X211004945. doi: 10.1177/0272684X211004945

Reer, F., Tang, W. Y., and Quandt, T. (2019). Psychosocial well-being and social media engagement: the mediating roles of social comparison orientation and fear of missing out. New Media Soc. 21, 1486–1505. doi: 10.1177/1461444818823719

Ringle, C., Wende, S., and Becker, J. (2015). SmartPLS 3 [software] . Bönningstedt: SmartPLS.

Ringle, C. M., Sarstedt, M., and Straub, D. (2012). A critical look at the use of PLS-SEM in “MIS Quarterly.” MIS Q . 36, iii–xiv. doi: 10.2307/41410402

Roberts, J. A., and David, M. E. (2020). The social media party: fear of missing out (FoMO), social media intensity, connection, and well-being. Int. J. Hum. Comput. Interact. 36, 386–392. doi: 10.1080/10447318.2019.1646517

Salehan, M., and Negahban, A. (2013). Social networking on smartphones: when mobile phones become addictive. Comput. Hum. Behav. 29, 2632–2639. doi: 10.1016/j.chb.2013.07.003

Sarstedt, M., and Cheah, J.-H. (2019). Partial least squares structural equation modeling using SmartPLS: a software review. J. Mark. Anal. 7, 196–202. doi: 10.1057/s41270-019-00058-3

Schinka, K. C., VanDulmen, M. H., Bossarte, R., and Swahn, M. (2012). Association between loneliness and suicidality during middle childhood and adolescence: longitudinal effects and the role of demographic characteristics. J. Psychol. Interdiscipl. Appl. 146, 105–118. doi: 10.1080/00223980.2011.584084

Shi, S., Mu, R., Lin, L., Chen, Y., Kou, G., and Chen, X.-J. (2018). The impact of perceived online service quality on swift guanxi. Internet Res. 28, 432–455. doi: 10.1108/IntR-12-2016-0389

Shoukat, S. (2019). Cell phone addiction and psychological and physiological health in adolescents. EXCLI J. 18, 47–50. doi: 10.17179/excli2018-2006

Shrestha, N. (2021). Factor analysis as a tool for survey analysis. Am. J. Appl. Math. Stat. 9, 4–11. doi: 10.12691/ajams-9-1-2

Stouthuysen, K., Teunis, I., Reusen, E., and Slabbinck, H. (2018). Initial trust and intentions to buy: The effect of vendor-specific guarantees, customer reviews and the role of online shopping experience. Electr. Commer. Res. Appl. 27, 23–38. doi: 10.1016/j.elerap.2017.11.002

Swar, B., and Hameed, T. (2017). “Fear of missing out, social media engagement, smartphone addiction and distraction: moderating role of self-help mobile apps-based interventions in the youth ,” Paper presented at the 10th International Conference on Health Informatics (Porto).

Tangmunkongvorakul, A., Musumari, P. M., Thongpibul, K., Srithanaviboonchai, K., Techasrivichien, T., Suguimoto, S. P., et al. (2019). Association of excessive smartphone use with psychological well-being among University students in Chiang Mai, Thailand. PLoS ONE 14:e0210294. doi: 10.1371/journal.pone.0210294

Tateno, M., Teo, A. R., Ukai, W., Kanazawa, J., Katsuki, R., Kubo, H., et al. (2019). Internet addiction, smartphone addiction, and hikikomori trait in Japanese young adult: social isolation and social network. Front. Psychiatry 10:455. doi: 10.3389/fpsyt.2019.00455

Tefertiller, A. C., Maxwell, L. C., and Morris, D. L. (2020). Social media goes to the movies: fear of missing out, social capital, and social motivations of cinema attendance. Mass Commun. Soc. 23, 378–399. doi: 10.1080/15205436.2019.1653468

Tehseen, S., Qureshi, Z. H., Johara, F., and Ramayah, T. (2020). Assessing dimensions of entrepreneurial competencies: a type II (reflective-formative) measurement approach using PLS-SEM. J. Sustain. Sci. Manage. 15, 108–145.

Tehseen, S., Ramayah, T., and Sajilan, S. (2017). Testing and controlling for common method variance: a review of available methods. J. Manag. Sci. 4, 146–165. doi: 10.20547/jms.2014.1704202

Tonacci, A., Billeci, L., Sansone, F., Masci, A., Pala, A. P., Domenici, C., et al. (2019). An innovative, unobtrusive approach to investigate smartphone interaction in nonaddicted subjects based on wearable sensors: a pilot study. Medicina (Kaunas) 55:37. doi: 10.3390/medicina55020037

Twenge, J. M., and Campbell, W. K. (2019). Media use is linked to lower psychological well-being: evidence from three datasets. Psychiatr. Q. 90, 311–331. doi: 10.1007/s11126-019-09630-7

Vallespín, M., Molinillo, S., and Muñoz-Leiva, F. (2017). Segmentation and explanation of smartphone use for travel planning based on socio-demographic and behavioral variables. Ind. Manag. Data Syst. 117, 605–619. doi: 10.1108/IMDS-03-2016-0089

Van Den Eijnden, R. J., Lemmens, J. S., and Valkenburg, P. M. (2016). The social media disorder scale. Comput. Hum. Behav. 61, 478–487. doi: 10.1016/j.chb.2016.03.038

Whaite, E. O., Shensa, A., Sidani, J. E., Colditz, J. B., and Primack, B. A. (2018). Social media use, personality characteristics, and social isolation among young adults in the United States. Pers. Indiv. Differ. 124, 45–50. doi: 10.1016/j.paid.2017.10.030

Williams, D. (2006). On and off the'net: scales for social capital in an online era. J. Comput. Mediat. Commun. 11, 593–628. doi: 10.1016/j.1083-6101.2006.00029.x

Keywords: smartphone addiction, social isolation, bonding social capital, bridging social capital, phubbing, social media use

Citation: Ostic D, Qalati SA, Barbosa B, Shah SMM, Galvan Vela E, Herzallah AM and Liu F (2021) Effects of Social Media Use on Psychological Well-Being: A Mediated Model. Front. Psychol. 12:678766. doi: 10.3389/fpsyg.2021.678766

Received: 10 March 2021; Accepted: 25 May 2021; Published: 21 June 2021.

Reviewed by:

Copyright © 2021 Ostic, Qalati, Barbosa, Shah, Galvan Vela, Herzallah and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sikandar Ali Qalati, sidqalati@gmail.com ; 5103180243@stmail.ujs.edu.cn ; Esthela Galvan Vela, esthela.galvan@cetys.mx

† ORCID: Dragana Ostic orcid.org/0000-0002-0469-1342 Sikandar Ali Qalati orcid.org/0000-0001-7235-6098 Belem Barbosa orcid.org/0000-0002-4057-360X Esthela Galvan Vela orcid.org/0000-0002-8778-3989 Feng Liu orcid.org/0000-0001-9367-049X

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Social Media Methods

Meet Dr Wasim Ahmed , a Visiting Fellow in Digital Business at Newcastle University Business School, who uses social network analysis to analyse social media.

Social media are understood to be “web-based platforms that enable and facilitate users to generate and share content, allowing subsequent online interactions with other users (where users are usually, but not always, individuals)” (Social Media Research Group, 2016). Social media platforms offer an ever-expanding resource for researchers and are increasingly used to recruit participants, generate data, and disseminate findings. Social media research is used within a wide range of disciplines including media and communication, critical data studies, sociology, political science, digital civics and practice related research, among many more.

There are many different ways to use social media platforms within research and the approach taken will affect the data collection and analysis techniques chosen. For example, using social media platforms to circulate surveys and find research participants has significantly different implications to using platforms to generate quantitative user data. There are a diverse range of quantitative, qualitative and mixed-methods approaches to conducting social media research.

Data generation methods include using application programming interfaces (APIs), purchasing data through official resellers, ‘scraping’ data from the website or screenshotting social media pages, to name a few (Mayr and Weller, 2017).

The type of data generated can vary widely according to the platform and purpose of the project and may include network data, tracked activities such as likes and shares, images, or screenshots from profiles (Mayr and Weller, 2017). Examples of analysis techniques used in social media research includes the coding of images and non-text data, narrative analysis of social media text, geospatial analysis and using software such as R to analyse big data.

There are many benefits associated with using social media as a research tool including the availability and volume of real-time data (such as geotagging) and the cost and time-effective nature of access when compared with other methods. Considerations of social media research approaches include the increasing governance by social media platforms, a lack of representation of populations (Ruths and Pfeffer, 2014) and the presence of automated ‘bots’ which can skew the validity of the data. These methods also provide new ethical issues to consider for example around consent, privacy, and data protection. The SAGE Handbook of Social Media Research Methods (2017) provides a useful overview of issues around ethics, data storage, related philosophies, and case studies of social media related research projects.

Majid Khosravinik - Senior Lecturer in Digital Media & Discourse Studies and Chair of Board of Studies
Sebastian Popa – Senior Lecturer in Comparative Politics
Steve Walls – Lecturer in Media and Cultural Studies

MCH2012: Analysing Social Media Interactions

MKT3012: Digital Marketing

Newcastle University Library Guide - https://libguides.ncl.ac.uk/socialmedia

Mayr, P., and Weller, K., (2017). Think Before You Collect: Setting Up a Data Collection Approach for Social Media Studies. In: Sloan, L., and Quan-Haase, A., (2017). The SAGE Handbook of Social Media Research Methods. London: SAGE Ch.8.
Ruths and Pfeffer, (2014), Social media for large studies of behavior, Science , 346(6213), pp.1063-1064.
Social Media Research Group, (2016). Using social media for social research: An introduction. (Accessed 15 January 2021) <Available at: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/524750/GSR_Social_Media_Research_Guidance_-_Using_social_media_for_social_research.pdf>

example of research methodology about social media

Toward Improved Methods in Social Media Research

Volume 1, Issue 1 | DOI: 10.1037/tmb0000005

Both academic and public interest in social media and their effects have increased dramatically over the last decade. In particular, a plethora of studies has been conducted aimed at uncovering the relationship between social media use and youth wellbeing, fueled by recent concerns that declines in youth wellbeing may well be caused by a rise in digital technology use. However, reviews of the field strongly suggest that the picture may not be as clear-cut as previously thought, with some studies suggesting positive effects and some suggesting negative effects on youth wellbeing. To shed light on this ambiguity, we have conducted a narrative review of 94 social media use and wellbeing studies. A number of patterns in methodological practices in the field has now become apparent: self-report measures of general statistics around social media use dominate the field, which furthermore often falls short in terms of ecological validity and sufficient use of experimental designs that would enable causal inference. We then go on to discuss why such practices are problematic in some cases, and more importantly, which concrete improvements can be made for future studies that aim to investigate the relationship between social media use and wellbeing.

Keywords: social media, methodology, wellbeing, objective data, self-report, qualitative research

Disclosure and Acknowledgment : The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. This work was supported by the European Research Council (ERC) Consolidator Grant [683262].

Open Access License : This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC- BY-NC-ND). This license permits copying and redistributing the work in any medium or format for noncommercial use provided the original authors and source are credited and a link to the license is included in attribution. No derivative works are permitted under this license.

Disclaimer : Interactive content is included in the online version of this article.

Contact Information : Correspondence concerning this article should be addressed to Nastasia Griffioen, Behavioural Science Institute, Radboud University, Montessorilaan 3, 6525 HR Nijmegen, The Netherlands. Email: [email protected]

Introduction

As profoundly social beings, humans crave social interaction to the extent that lack thereof affects us negatively in all kinds of ways. People experience reduced stress when social support is abundant (e.g., Cohen & Wills, 1985 ) and are at increased risk of death when social ties are scarce (Berkman & Syme, 1979) . The importance of social connections is also illustrated by the incredible popularity of social media. There are social networks available in almost any domain of our lives (e.g., dating—Tinder; careers—LinkedIn; games—Discord). The most popular networks are not just used by many; they are used by most: In the United States alone, roughly three-quarters of the population are members of one or more social networks (Pew Research Center, 2018a) , and almost 3.48 billion people worldwide are actively using social media ( Kemp, 2019 , January 30). Zooming in on the younger generation, we see an astounding 94% of 18–24-year-olds and 85% of 13–17-year-olds (Pew Research Center, 2018) reporting using an online social medium.

The fervent adoption of these platforms, especially among the younger generations, has sparked interest as well as concern, primarily among parents and schools. Many worry that teenagers are “glued to their phones” (e.g., Zolfagharifard, 2017 , August 31), paying less and less attention to the physical world around them. Indeed, children’s and adolescents’ lives look different from those of their parents. The activities they engage in may seem odd, and perhaps detrimental, in the eyes of the older generations that did not grow up using smartphones and tablets. Some studies even seem to suggest that current teenagers prefer to connect with peers through their phones rather than in person (Common Sense Media, 2018) . Digital technologies, such as social media, do not just “complement” previous ways of communicating, but have instead replaced their analogue counterparts almost entirely, as is the case with emails almost wholly replacing letters ( Schmid, 2011 , October 3). Some even argue that as a result of such an attachment to mobile media, the majority of UK children spend less time outside than prison inmates do ( Carrington, 2016 , March 25).

Following a growing concern among the general public, research on the topic of social media in the last decade has increased as well. Findings on the relationship between social media use and well-being are, however, far from straightforward. Recently, a number of reviews have been published, and one thing most of them have in common is the fact that conclusions regarding the effect of social media use on well-being seem hard to draw (e.g., Erfani & Abedin, 2018 ). One review even concluded that the field of social media and well-being research is dealing with “contradictory evidence, while revealing an absence of robust causal research regarding the impact of social media on mental wellbeing of young people” ( Best et al., 2014 , p.1).

The field’s ambiguity is puzzling, but the ways in which the field has conducted its studies so far are likely to offer an explanation: Research methodologies are not just a tool to perceive and assess, but instead play a large role in what is perceived and how it is assessed. This article aims to give an overview of the research practices currently being applied in social media use and well-being research and shed light on the implications of study design choices. We will discuss the dominant presence of self-report and the types of data that are gathered, as well as the importance of ecological validity and causality. A clear sense of the way in which social media and well-being research is currently being conducted should be instrumental in facilitating the field to build more reliable, robust, and informative studies addressing the link between social media use and well-being. Our synthesis of methodological trends will be followed by a brief discussion of the strengths and weaknesses of these methodologies as well as suggestions for improvement that will enable us to answer questions like “What do adolescents do on social media?” “What do adolescents expect from social media?” “How do they feel when interacting on social media?” and “Why do they use social media at all?”

Current Research Practices

Literature search specifications.

The search for articles on the topic of social media use and well-being was conducted in three different databases—PsycINFO, Web Of Science, and MEDLINE. Using the search queries “social media use” AND (“wellbeing” OR “well-being”) and restricting the search to 2010–2018, we found a total of 129 articles. To investigate the studies most relevant to the current social debate around social media use, we focused on general, healthy population samples, and thus removed studies focusing on disordered samples (e.g., schizophrenia or attention deficit hyperactivity disorder samples). Duplicates, non-peer-reviewed, nonempirical, and/or non-English articles were also excluded. Cross-checking the reference lists of (review) articles with our selection brought the total number of articles in our review to 72. During the time of writing, eight more articles were included (up to June 2019) and three more articles were added during the review process, bringing up the total number of articles included in our review to 83. Because some articles feature more than one study, the total number of empirical studies included in our review is 94. See Figure 1 for a schematic overview of the search process.

Figure 1. Overview of the systematic search process.

Patterns in Research Practices

First, we found that the field is dominated by studies relying on self-report measures. Of the reviewed studies, 81.9% ( n = 77 out of 94) quantified social media use by asking participants to retrospectively report on their social media use. Only 6.4% of the studies ( n = 6 out of 94) used some form of objective assessment of social media use, based, for example, on Facebook’s activity logs (e.g., Burke et al., 2010 ) or other types of recordings of people’s activity on social media (e.g., Verduyn et al., 2015 ).

When looking closer at the types of self-report measures (i.e., questionnaires) of social media use, it is striking that many studies either seem to use questions developed by the researchers for the first time in their particular study, or use selected (and often adapted) questions from already available scales such as the Facebook Questionnaire (Ross et al., 2009) . The questions used are most often along the lines of “On average, how much time per day/week do you spend on Facebook/social media?” and “How often do you do the following things on social network sites?” (listing a number of activities such as posting and chatting). This is in line with our observation that mostly general measures of social media use are collected (see below). Few studies used a preexisting, validated questionnaire, the most frequently used being the Facebook Intensity Scale ( n = 7 out of 79) (Ellison et al., 2007) .

Other scales that have been used include the Instagram Activity Scale ( Yang, 2016 ; used by Yang & Robinson, 2018 ), the Multidimensional Scale of Facebook Use ( Frison & Eggermont, 2015 ; used by Frison & Eggermont, 2015 ); (Faelens et al., 2019) , the Bergen Facebook Addiction Scale ( Andreassen et al., 2012 ; used by Dhir et al., 2018 ), the Bergen Social Media Addiction Scale ( Andreassen et al., 2017 ; used by Worsley et al., 2018 ), the Facebook Questionnaire ( (Ross et al., 2009) ; used by (Ryan & Xenos, 2011) ; (Simoncic et al., 2014) , the Social Networking Survey ( Davila et al., 2012 ; used by Davila et al., 2012 ), the Multidimensional Facebook Intensity Scale ( Orosz et al., 2016 ; used by Phu & Gow, 2019 ), and the Facebook Activity Measure ( Shaw et al., 2015 ; used by Shaw et al., 2015 ). As becomes apparent from this list, many of these scales are used in only one or two studies, and there is thus little overlap between the studies in terms of the actual scales that are used. Similarly, for single questions there seems to be little consensus on wording, even though many of these questions aim to gauge the same thing, for instance, time spent on social media. This understandably makes it difficult to draw conclusions across studies and may play a role in explaining why superficially similar studies produce different findings.

Moreover, almost three in five studies focused solely on general measures of social media use ( n = 55), such as the frequency of use, preferences for certain social media platforms, and average time spent on social media per week. In contrast to general measures of social media use, some have suggested that the way in which social media are used is of consequence to its effects; specifically, active use is defined as posting, commenting, and sharing status updates, whereas passive use is defined by a more uninvolved consumption of social media content, for instance, by browsing and scrolling (Thorisdottir et al., 2019) . Indeed, out of the 39 studies that looked at more specific types of use and/or experiences, roughly a quarter ( n = 11) differentiated between active and passive uses of social media, and three-quarters ( n = 28) looked at more specific social media behaviors (e.g., posting), but not necessarily with a focus on the active versus passive dichotomy. Within this group of 39 studies, a group of articles ( n = 4) collected specific metrics such as frequencies per social media activity type (Kim & Kim, 2017) , but aggregated them into general metrics for subsequent analysis. It is thus clear that relatively little attention is being paid to the details of social media use, despite recent indications ( Burke & Kraut, 2013 ; Burke et al., 2011 ; Huang, 2010 ) that this may be exactly what is needed to get a better understanding of what is going on and move the field forward.

In line with the fact that the vast majority of studies rely on retrospective accounts of general measures of social media, we have found that only a small proportion of the studies, about 18.1% ( n = 17 out of 94), attempted to incorporate (parts of) the experience of social media use in their studies. In some of these cases, study design elements were less than ideal from an ecological validity point of view. For example, in some studies, participants were overtly restricted in the ways they could behave, for instance, by prohibiting participants to share posts on Facebook ( Tobin, et al. 2015 ; for other examples of restriction, see Deters & Mehl, 2013 ; Yuen et al., 2019 ; Verduyn et al., 2015 ). Other studies clearly manipulated expectations, for instance, by telling participants to expect comments on their posts from coparticipants (thus rendering the fact that participants felt bad when these comments remained absent not particularly surprising, see Tobin et al., 2015 ). In some studies, manipulations may have been less effective than intended given the aim of the manipulation. For instance, in a study by Vogel et al., 2014 , an (intended) downward social comparison target (i.e., someone who is perceived as inferior to the self) may well have been interpreted as an upward social comparison target (i.e., someone who is perceived as superior to the self) by some participants. In sum, truly realistic implementations of social media use are still rare in the present body of literature. This is problematic because it is important that we draw conclusions based on examples that accurately reflect real life.

Last, we only found 17 studies (18.1%) that used an experimental setup to examine the link between social media use and well-being. The remaining 88.3% ( n = 77) used observational methods rather than experimental ones. This is a serious limitation given that strong claims are being made about the detrimental effects of social media use in the popular media (e.g., Barr, 2019 , October 10; Twenge, 2018 ). One notable subgroup in these observational studies used an experience sampling methodology (ESM) ( n = 5) (Csikszentmihalyi & Larson, 1987) in which participants’ social media use and well-being were assessed through self-report multiple times a day over an extended number of days ( Kross et al., 2013 ; Steers et al., 2014 ; Verduyn et al., 2015 ; Wenninger et al., 2014 ). Another 4 out of these 77 studies used a longitudinal design ( Booker et al., 2018 ; Frison & Eggermont, 2015 ; Heffer et al., 2019 ; Matook et al., 2015 ), which is an important step toward the ability to draw conclusions about causality, but still surprisingly rare in the body of studies that we have reviewed. See Figure 2 for a summary of the methodological patterns discussed.

Figure 2. Frequency of occurrence of methodological characteristics

Thus, we have found methodological patterns emerging from the literature that can be summarized as follows: The field predominantly relies on self-report measures of general statistics around social media use, and often falls short in terms of ecological validity and sufficient use of experimental research that would enable causal inference. For an overview of the studies that were reviewed and their methodological characteristics, see Table 1 . Now that the general methodological landscape of social media use research has been painted, we move on to address its features in more detail and look ahead to the horizon of future research by offering suggestions for improvement in studies yet to come.

Pitfalls and Solutions in Current Research Practices

Self-report data.

The first and perhaps most evident pitfall currently present in the field of social media use and well-being research is its overreliance on self-report data. Self-report continues to be, by far, the dominant measure in social media research. Of course, psychology as a field has been using self-report from its inception and has been discussing its merits (e.g., understanding people’s own perception of their behaviors) and limitations (e.g., biases, social desirability, single-subject shared variance, and so on; e.g., Rosenman et al., 2011 ; Allport, 1927 ) for equally as long. Thus, the problem and ubiquitousness of self-report measures is not particularly unique to social media research. In fact, it is curious that our 6.4% of studies that measure actual behaviors in social media use closely resemble Doliński's (2018) observation that more generally, across the field of personality and social psychology, behavioral measurement was equally rare, with only 6% of studies including behavioral measures in their broader review.

While issues with self-report are thus not new, recent studies have highlighted the severity of the problem when using this type of data to make any inferences about social media’s causal relations to well-being. Specifically, recent studies indicate that there is a low correlation between people’s subjectively reported time spent on phones and objective data extracted from the phones themselves ( Boase & Ling, 2013 ; Ellis, 2019 ). Similarly, the overlap between psychometric scales measuring phone use and objective behavior is “generally poor,” with some correlations as low as .2 (Ellis et al., 2019) .

This may not seem surprising in light of long-lasting debates around the merits and limitations of self-report, but the implications are vast: Decisions around social media use—and more generally screen time use—are being made on all sides. Parents panic about their children’s well-being, a panic that is fueled by mostly correlational studies that suffer from the problems outlined above. Policy decisions are being made (e.g., in China, see “China province to ban homework,” (2019) ; “China to impose curfew,” 2019 ) that affect millions of children and teenagers, while real concerns about (mis)use remain unaddressed. For example, children may be visiting harmful websites or being irresponsible about what data they share when they are online but are likely reluctant to share such details when asked. It is thus safe to say that real-world decisions are being made at a large scale, and we as a field need to make sure that the information at the root of those decisions is reliable and valid. It has become clear now that if we want reliable data to answer questions like “Does time spent on social media relate to lower well-being?” we likely need to look to sources other than self-report.

It is remarkable how few studies have leveraged the digital nature of social media when gathering quantitative data regarding social media use. The devices on which social media are accessed gather and store large amounts of (in principle, objective) data pertaining to the activities being carried out on them (Piwek et al., 2016) . The use of such data is precisely one of the solutions we propose here. Recently, social media companies have made it easier for their users to access their own data. Facebook, Instagram, Twitter, and Snapchat now all provide their users with the option to download their data. See Table 2 for an overview of the “Download My Data” functionality offered by each of the social media platforms. Since then, several studies in the field of social media use and well-being have exploited the opportunity to access personal logs (e.g., Burke & Kraut, 2016 ; Park et al., 2016 ; Shakya & Christakis, 2017 ), but they remain few and far between. In addition to a downloadable “data dump” and other features such as Facebook’s “Activity Log”—which contains a slightly different array of variables (e.g., likes from others directed at your own posts, and who follows you)—some information may be available within the social media app itself. By taking screenshots or screen recordings of the social media app that is being used, relevant social media behaviors and information can be assessed in an objective manner (e.g., what sorts of posts are most prominent in a user’s news feed: those by friends or those by companies and pages?).

In some cases, however, none of these options are sufficient or appropriate for the desired study design. For instance, if assessment of social media use needs to dynamically interact with other elements of the study, after-the-fact data logs will not work. This could be the case when researchers want a questionnaire on the phone to be triggered by the participants’ starting to engage with a social media app, or if the researchers want a message for the participant to pop up after a certain amount of time has been spent on a specific social media platform. One way to solve this problem is to leverage “application programming interfaces” (APIs) offered by social networks that allow third-party apps to interact with information from these networks (Lomborg & Bechmann, 2014) . Such data connections between a social network and other companies are often used to achieve targeted advertisement. APIs also enable the creation of custom-made solutions, such as apps that serve as a portal through which participants will use their preferred social platform for the duration of a study. An environment could be created that mirrors the social media platform(s) of interest, forming a hub where all platforms come together, while allowing for the collection of specific data that are not otherwise accessible (such as timestamps for when a social network app was opened). This way, the researcher can continuously (and dynamically) gather information about everything the user does and sees. In addition, communication can be set up between the portal app and another system to, for instance, trigger questionnaires or manipulations.

That being said, a recent article by John & Nissenbaum (2019) has pointed out that certain aspects of social media use such as “disconnectivity” (i.e., actions such as “unfriending” somebody or “unliking” content) do not seem to be well represented within APIs. When exploring the use of APIs for research purposes, it seems important to determine if these APIs will be accessible to public researchers and able to deliver the necessary information. While all the suggestions that we have discussed here may have downsides such as effort or cost, overcoming these significant obstacles seems critical for the sake of rigorous and reliable science if more convenient and low-cost options are insufficient or inappropriate for the study at hand.

We would like to mention, however, that more (and better) data comes with its own potential pitfalls and ethical considerations, such as those of privacy. Social media use data—and in particular the data that we propose is of most value—is often highly sensitive and might contain information not only about the participant who has given consent for its use, but also about the participants’ social ties whose consent is not obtained. It is of vital importance to make sure that the privacy of all parties involved is guaranteed, or if anonymization for whatever reason is impossible, at least that full transparency toward participants is observed (e.g., specifics about the type of data that will be gathered, what it will be used for, and who will be able to access it). These considerations have become increasingly complex in light of the relatively recent developments toward procedures that promote open science. Sharing data openly in repositories, for instance, is directly at odds with the privacy of participants when it comes to sensitive data. This issue is complex and deserves a deeper analysis of the balance between risks and mitigations, but they are currently beyond the scope of the present work. There are, however, important strides being taken toward a coherent strategy to navigate these security and privacy matters (Dennis et al., 2019) .

Specificity of Social Media Use Data

The second pattern that has emerged from our review of the literature revolves around the level of detail with which social media use has been investigated so far. In light of the inconclusiveness of the current body of findings, some have suggested that general metrics and the global, increasingly vague concept of “screen time” need to be replaced with designs, methods, and analytical techniques that can concretely differentiate between different kinds of social media activities ( Burke et al., 2010 ). Social media contain an immense range of functionalities, and the experiences people have when interacting with social media can be extremely diverse. What social media mean and offer to their users has been developing rapidly ever since they appeared. Classmates.com, the first social network, was an instant hit when it launched in 1995, but its features—which initially consisted of being able to simply, and only, track down school yearbooks—will seem unsatisfactory to modern-day social media users. Many more “sharing” functions have now been added to the online social media arsenal, such as the sharing of music, feelings, activities, locations, friends, photos, and even belongings. Users are able to share almost all and any aspects of their lives while viewing often carefully curated snapshots of the lives of others. Not taking this diversity into account means losing sight of information that could explain differences in social media’s effects.

We propose that behaviors and experiences should be viewed on a more specific, and thus functional, level. For instance, by distinguishing between actions that are “active” versus “passive”, or distinguishing between the effects of viewing posts that come from different types of sources (e.g., friends versus celebrities), researchers can delve deeper into the rich nature of social media in attempts to determine why and when social media may affect well-being. To help researchers navigate the diversity of social media behaviors, we created an overview of the functionalities that social media users can engage in on the most-used social media platforms (see Table 3 ).

When designing future studies investigating social media use, it is similarly important to be aware of selection biases pertaining to the platform being studied. For instance, while Facebook has dominated the social media landscape for a long time, teenagers aged 13–17 have, to a large degree, abandoned Facebook in favor of Instagram and Snapchat (Pew Research Center, 2018b) . It is therefore important to ask questions such as “Do users of different platforms differ in meaningful ways?” and “Is there something about the users’ goals that leads them to use different social media?” Whether such shifts in platforms’ user bases are problematic for a study’s design naturally remains to be assessed by researchers individually.

It should be noted, however, that attention to more and more fine-grained details of social media use alone will not do. A greater attention to detail paradoxically also entails that we pay attention to the larger behavioral patterns that surround social media use, so that we may understand the contexts in which these media are being used, separately and in parallel. Young people do not just use one app; they use tens of different apps, sometimes at the same time, and we need to be able to capture this variety of use to better understand the entire digital ecosystem and users’ connections to it. This, too, requires that we gather fine-grained and objective data, for instance, through the use of aforementioned APIs or screen recordings ( Reeves et al., 2019 ; Ram et al., 2020 ).

Last, being specific about what children and teenagers do on social media and how it makes them feel also requires probing their subjective experiences and getting as close as possible to their actual lived experiences. Combining the strengths of the reliability of objective data with the depth and sensitivity to context afforded by subjective approaches is a challenge because it would require the integration of multiple methods. Nevertheless, the first steps toward such approaches have already been made, for instance, by Piwek & Joinson (2016) , who have investigated how and with whom Snapchat is used by adolescents. One promising avenue to further this direction of research along is through the use of “stimulated recall.” Bloom ( 1953 , p. 161) expressed that the primary aim of the method is “that the subject may be enabled to relive an original situation with vividness and accuracy if he is presented with a large number of the cues or stimuli which occurred during the original situation.” Stimulated recall is an approach in which the benefits of quantitative research (i.e., attention to context, motivations, and subjective experience) are supported by objective data. Regular retrospective self-report regarding behaviors (or even feelings) is—as we have seen—a risky business given the difficulty people have with accurately recalling past events. Stimulated recall relies on recall immediately following the event of interest. Participants are supported when recalling relevant aspects of this past experience through the use of materials such as audio and/or video recordings and physiological data. Such methods have often been applied in educational sciences (e.g., Calderhead, 1981 ; De Witt, 2008 ; Meier & Vogt, 2015 ) and in user experience research to systematically assess what users think and feel during certain actions or events.

A concrete example in the context of social media use research may be useful for demonstrating the power of the stimulated recall method. We recently used this methodology in a study in which we asked the participants to wait in a room for a short amount of time (10 min), during which we collected video footage of their actions. Following the waiting/monitoring period, we informed the participants about the real aim of the study (i.e., mapping out what adolescents do on their phones and on social media in particular, for which reasons, and how it makes them feel). If participants consented, we proceeded with a stimulated recall interview phase. During this phase, we used the video footage of their activities as well as in-app logs to help the participants answer a number of structured questions about their phone and social media use during the waiting period (for a more detailed description of this particular implementation, see Griffioen et al., 2020 ). By implementing a highly structured stimulated recall interview in combination with objective data retrieved, this methodology allows researchers to address the current lack of reliable, objective information in the field. It also helps us focus more on the content, function, and processes of social media use and provide a structured way of gathering these data.

Ecological Validity

A third and related problem pervading current social media and well-being research pertains to its ecological validity. When studying a behavior that occurs in day-to-day life as prominently and frequently as social media use, it is important to make sure that the context in which it is studied reflects the character of these everyday situations. This is especially important when laboratory studies on this subject are conducted because these contexts are most dissimilar from everyday life. Most importantly, social media are steeped in perpetually social, personally meaningful, and emotionally salient contexts. These contexts, however, are rarely investigated, and the focus so far has lied predominantly on the technology itself, not on its function for its users: Only 17 of the many experiments that we reviewed attempted to take social and emotional contexts into consideration and many of these studies have serious limitations (see section “Patterns in Research Practices” for examples). The key elements of the experience and use of social media are often overly controlled or even overlooked in laboratory experiments, even though it is essential to keep the central, functional feature of social media use in mind: They are fundamentally social platforms with social interaction and relationships as their key purpose. While field research is an important avenue for ensuring that the context of measurement is ecologically valid, laboratory studies are nevertheless sometimes required to assess causal links between social media use and, for instance, aspects of well-being. The discussion here thus revolves not only around increasing ecological validity by conducting field studies, but also by conducting laboratory studies in a better, more context-sensitive way. Ensuring that laboratory social media experiences reflect real-life use (i.e., that they are ecologically valid) requires that we improve our understanding of what it feels like to use social media, both in the moment and in past experiences, with the goal of incorporating those key elements in laboratory re-creations.

First, to understand what social media use evokes in the user in the moment, it is important to acknowledge that there is a tremendous amount of salience tied to social information. This is unsurprising because we are social creatures, and rely, to a large extent, on other people ( Baumeister & Leary, 1995 ; Beckes & Coan, 2011 ; Berkman & Syme, 1979 ; Bloomberg et al., 1994 ; Cohen & Wills, 1985 ). The curated nature of social media further augments this salience because users receive information from sources that are important to them, be they close friends, family, or celebrities. Indeed, the social salience of social media has been previously acknowledged: Social media are infamous for their role in eliciting social comparison ( Appel et al., 2015 ; Chow & Wan, 2017 ; Fardouly et al., 2015 ; Haferkamp & Krämer, 2011 ; Jang et al., 2016 ; Nesi & Prinstein, 2015 ). When the social salience experienced in social media is absent from its re-creations, a study no longer provides insight into the real-life processes related to social media use.

One way to investigate the determinants of social media salience is to assess arousal, for instance, through physiological measures such as galvanic skin response (GSR) (Bach et al., 2010) , pupil dilation, heart rate ( Bradley et al., 2008 ; Wang et al., 2018 ), or eye-tracking in combination with facial expressions, when people are viewing social media content. Through structured and thorough debriefing afterward (e.g., by implementing a variation of the stimulated recall method discussed earlier), it is possible to assess which feelings and thought processes are being evoked by the information that one encounters on social media (e.g., “I saw a post by a friend dedicated to her mother, and I felt happy and sad at the same time”), and for which reasons (e.g., “I felt happy and sad because that was something I went through myself, and I recognized myself in her story”). Such an understanding of the emotions and thoughts taking place during social media experiences is essential if we want to be able to re-create these experiences in laboratory settings, and thus create ecologically valid research contexts.

Second, the role of prior experiences and future expectations when using a medium in which a lot of social information is encountered is often overlooked. Participants are not blank slates; they have gained extensive prior experiences in the (online) social realm. We propose that for laboratory experiments around social media use to be most informative and ecologically valid, a participant’s prior experiences on social media need to be taken into account. Only then will we be able to meaningfully interpret and understand the ways in which participants respond to events in experimental social media contexts, and why they do so. Given that prior experiences will inform future expectations, well-being is likely related to these experiences and expectations for what the future will bring. Feelings such as anxiety and depression, for example, are marked by a negativity bias regarding future events (Korn et al., 2014) . Since much of social media use research is related to its effects on well-being, it is striking that fairly little attention has so far been paid to people’s subjective experiences, motivations, and expectations when using social media.

In social media research, assessing what such prior beliefs or expectations look like can be as simple as asking participants what sort of information they expect to see, how they expect to feel, and why. These expectations may or may not be related to participants’ self-evaluative beliefs, and thinking about how their beliefs are updated throughout social media interactions may be informative in investigating the link between their prospective social media use and well-being. Information about such “priors” can, for instance, help us understand why in some individuals we seem to find detrimental effects of social media, while we do not in others. Such methods—to our knowledge—have not yet been implemented in social media research, but there is interesting research in adjacent fields that might offer different ways of thinking about how we can assess sequential social learning processes and what shape they take.

In a study by Will and colleagues (2017) , for instance, computational models helped determine that the way in which we update our self-evaluative beliefs is similar to the way in which we learn about others. Similar models could be applied to social media use research to form and test predictions about how contact with and processing of different salient aspects of social media (e.g., content of posts, or types of social ties encountered) might change expectations about future social media visits. These expectations, in turn, might affect the extent to which users internalize social media content during those future visits (i.e., the extent to which social media end up affecting their well-being). In sum, forming models—be they conceptual or formal—of social media experiences at longer temporal scales will provide us with a better understanding of real-life social media use and its relationship to well-being.

In addition to objective data and ecological validity, the ability to test causality is important to draw conclusions about the effect of social media on well-being. As we have discussed, however, the literature is mostly dominated by observational designs, which—similar to self-report measures—can be insightful if implemented appropriately. Observations enable us to study people in real, everyday situations, thus providing the opportunity to uncover behaviors or phenomena that would otherwise remain unnoticed (Allen, 2017) . However, observational designs have one major drawback: They do not allow for causal inference. Consequently, experimental or (semi-)longitudinal designs are important to provide us with information about whether a relationship might be causal.

In our review of the empirical literature, we found only nine studies, four longitudinal and five ESM studies, that have attempted to circumvent the primary downside of observational designs. More experimental designs are needed, and researchers designing these studies will benefit from understanding participants’ prior and current experiences and expectations, if manipulations are to be effective and realistic. In particular, ESM (Csikszentmihalyi & Larson, 1987) offers interesting avenues for approximating some sense of causality while still maintaining more of the ecological validity often found in observational studies. Through repeated measures of the constructs of interest (e.g., daily stress), ESM provides a way to minimize response biases while still measuring variables of interest within the participants’ natural environment (Riediger, 2009) . Although usually no experimenter-induced manipulations take place during the ESM period, the extended nature of the measurements also allows for tracking of the order in which certain events have taken place, which helps form a sense of which events in a person’s life (e.g., stressful experiences) have an effect on other elements of their life (e.g., mood).

Modern ESM studies leverage the fact that most people carry around smartphones on which they can receive texts, emails, and links to websites containing questions, and through which data can be saved directly to a secure database. No wonder, then, that there is a growing number of studies that implement ESM to gather data about people’s well-being on the same device that is their portal to the digital social world (e.g., Steers et al., 2014 ; Verduyn et al., 2015 ; Wenninger et al., 2014 ). While ESM is promising, and researchers are continuously working hard to improve ESM reliability (e.g., Berkel et al., 2020 ), there are a number of adjustments that we feel that can be made to improve the quality of research given the nature of social media use.

Social media are being used often at many different moments during the day, and some research suggests that a portion of this use may be happening almost subconsciously ( Lin et al., 2015 ; Montag et al., 2015 ), for instance, when waiting for a bus, swiping around on one’s phone, looking for something to read or do. Thus, even if you are asked about your use and experiences in social media five random times a day, you are likely to have a hard time remembering what it is exactly that you did or saw when you last visited a social network on your phone. To further minimize this recall bias, we suggest that ESM measurements in future social media studies could be triggered by specific events such as the use of social media itself (for instance, immediately following the closing of a social media app). Although such event-triggered ESM methods do not seem to have been implemented yet (even outside the field of social media research), we propose that they are a critical improvement on the traditional random-measurement approach implemented by most ESM studies. We further argue that—given our suggestions on gathering objective data regarding social media, in particular regarding the use of APIs—such event-based triggers are feasible and will further improve our ability to draw meaningful conclusions from ESM data.

In addition, we urge researchers who are implementing ESM in their study of social media and well-being to include objective measures of what it is that participants do and see (on social media) in their analyses (rather than only using such information to trigger event-based questions). Such data, in contrast to the data necessary for event-based triggers, could be collected retrospectively using the data logs that were mentioned in section “Self-Report Data”. This way, ESM questions can be aimed at assessing the qualitative side of people’s social media use (e.g., “How did you feel while reading other’s posts on social media, and why?”) when these experiences are most “fresh”, whereas objective data can tell us what it is exactly that people were doing and how often/for how long. The combination of objective measures of use and/or information encountered on social media and well-timed assessments are a promising avenue that needs to be explored. Ultimately, such methods can allow researchers in the field of social media use and well-being to find the answers not only to questions like “How do particular social media experiences relate to later mood and well-being?” but also to questions like “Do adolescents use social media differently depending on their mood?” “Do adolescents who feel depressed search for regulating social experiences on social media?” and “Does social media use elevate feelings of anxiety and stress or does it help regulate those feelings?” These are the questions that are at the core of social media use and well-being research.

While there is a lot of attention to (and concern about) social media and their effects, the link between social media use and well-being is far from well understood. To shed light on the state of the social media use and well-being literature, we synthesized the methodological characteristics of empirical studies conducted since 2010. In our literature review, we identified patterns that are present in this field which require improvement and adjustment to the still relatively new and poorly understood context of social media. Unrealistic and highly artificial research contexts are often the default designs in the field. Observational studies that lack sufficient ecological validity and the possibility of causal inference are abundant, whereas experimental work is scarce. Moreover, the function of social media use and specific ways in which that use addresses users’ goals are understudied, and self-report seems overused even though these reports are poorly related to objective measures.

There is a need for improvement of the research methodologies applied in this field, especially given the great weight assigned to studies examining the link between social media use and well-being. As with most new technological phenomena, a great deal of suspicion has been formed regarding what the use of social media does to the mental well-being of children, teenagers, and young adults. Policy changes (e.g., see World Health Organization, 2019 for guidelines issued regarding general screen time), clinical classifications (e.g., the ICD’s and DSM’s potential inclusion of internet addiction; Poli, 2017 ), and parenting guidelines (e.g., Elmore, 2018 , March 15) are being founded on a body of literature that we have demonstrated is not yet strong enough to bear the burden of proof for these large-scale implementation strategies.

However, there is a substantial number of ways in which these improvements can be made. Staying as close as we can in our studies to the real experience of what it means to interact with others on social media is of paramount importance. In addition, there is room for a lot more specificity in research into social media and ensuring that objective and reliable data gathered are all research goals that can be achieved in the future of social media use and well-being research. We hope that this article provides researchers that are examining the link between social media use and well-being with some useful suggestions for how to implement methodological improvements. With methodological innovations that are becoming increasingly accessible to all researchers, we are optimistic that the new generation of emerging studies on social media use and well-being will provide powerful and timely insights into these complex relations.

Table 1: Methodological Overview of Reviewed Studies

Table 2: data log overview of the biggest social media platforms, table 3: functionality overview of the biggest social media platforms.

Received January 21, 2020 Revision received March 27, 2020 Accepted April 09, 2020

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Research Article

Social impact in social media: A new method to evaluate the social impact of research

Roles Investigation, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Journalism and Communication Studies, Universitat Autonoma de Barcelona, Barcelona, Spain

Affiliation Department of Psychology and Sociology, Universidad de Zaragoza, Zaragoza, Spain

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

Affiliation Department of Sociology, Universitat Autonoma de Barcelona, Barcelona, Spain

Affiliation Department of Sociology, Universitat de Barcelona (UB), Barcelona, Spain

Cristina M. Pulido,
Gisela Redondo-Sama,
Teresa Sordé-Martí,
Ramon Flecha

Published: August 29, 2018
https://doi.org/10.1371/journal.pone.0203117
Reader Comments

The social impact of research has usually been analysed through the scientific outcomes produced under the auspices of the research. The growth of scholarly content in social media and the use of altmetrics by researchers to track their work facilitate the advancement in evaluating the impact of research. However, there is a gap in the identification of evidence of the social impact in terms of what citizens are sharing on their social media platforms. This article applies a social impact in social media methodology (SISM) to identify quantitative and qualitative evidence of the potential or real social impact of research shared on social media, specifically on Twitter and Facebook. We define the social impact coverage ratio (SICOR) to identify the percentage of tweets and Facebook posts providing information about potential or actual social impact in relation to the total amount of social media data found related to specific research projects. We selected 10 projects in different fields of knowledge to calculate the SICOR, and the results indicate that 0.43% of the tweets and Facebook posts collected provide linkages with information about social impact. However, our analysis indicates that some projects have a high percentage (4.98%) and others have no evidence of social impact shared in social media. Examples of quantitative and qualitative evidence of social impact are provided to illustrate these results. A general finding is that novel evidences of social impact of research can be found in social media, becoming relevant platforms for scientists to spread quantitative and qualitative evidence of social impact in social media to capture the interest of citizens. Thus, social media users are showed to be intermediaries making visible and assessing evidence of social impact.

Citation: Pulido CM, Redondo-Sama G, Sordé-Martí T, Flecha R (2018) Social impact in social media: A new method to evaluate the social impact of research. PLoS ONE 13(8): e0203117. https://doi.org/10.1371/journal.pone.0203117

Editor: Sergi Lozano, Institut Català de Paleoecologia Humana i Evolució Social (IPHES), SPAIN

Received: November 8, 2017; Accepted: August 15, 2018; Published: August 29, 2018

Copyright: © 2018 Pulido et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The research leading to these results has received funding from the 7th Framework Programme of the European Commission under the Grant Agreement n° 613202 P.I. Ramon Flecha, https://ec.europa.eu/research/fp7/index_en.cfm . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The social impact of research is at the core of some of the debates influencing how scientists develop their studies and how useful results for citizens and societies may be obtained. Concrete strategies to achieve social impact in particular research projects are related to a broader understanding of the role of science in contemporary society. There is a need to explore dialogues between science and society not only to communicate and disseminate science but also to achieve social improvements generated by science. Thus, the social impact of research emerges as an increasing concern within the scientific community [ 1 ]. As Bornmann [ 2 ] said, the assessment of this type of impact is badly needed and is more difficult than the measurement of scientific impact; for this reason, it is urgent to advance in the methodologies and approaches to measuring the social impact of research.

Several authors have approached the conceptualization of social impact, observing a lack of generally accepted conceptual and instrumental frameworks [ 3 ]. It is common to find a wide range of topics included in the contributions about social impact. In their analysis of the policies affecting land use, Hemling et al. [ 4 ] considered various domains in social impact, for instance, agricultural employment or health risk. Moving to the field of flora and fauna, Wilder and Walpole [ 5 ] studied the social impact of conservation projects, focusing on qualitative stories that provided information about changes in attitudes, behaviour, wellbeing and livelihoods. In an extensive study by Godin and Dore [ 6 ], the authors provided an overview and framework for the assessment of the contribution of science to society. They identified indicators of the impact of science, mentioning some of the most relevant weaknesses and developing a typology of impact that includes eleven dimensions, with one of them being the impact on society. The subdimensions of the impact of science on society focus on individuals (wellbeing and quality of life, social implication and practices) and organizations (speeches, interventions and actions). For the authors, social impact “refers to the impact knowledge has on welfare, and on the behaviours, practices and activities of people and groups” (p. 7).

In addition, the terms “social impact” and “societal impact” are sometimes used interchangeably. For instance, Bornmann [ 2 ] said that due to the difficulty of distinguishing social benefits from the superior term of societal benefits, “in much literature the term ‘social impact’ is used instead of ‘societal impact’”(p. 218). However, in other cases, the distinction is made [ 3 ], as in the present research. Similar to the definition used by the European Commission [ 7 ], social impact is used to refer to economic impact, societal impact, environmental impact and, additionally, human rights impact. Therefore, we use the term social impact as the broader concept that includes social improvements in all the above mentioned areas obtained from the transference of research results and representing positive steps towards the fulfilment of those officially defined social goals, including the UN Sustainable Development Goals, the EU 2020 Agenda, or similar official targets. For instance, the Europe 2020 strategy defines five priority targets with concrete indicators (employment, research and development, climate change and energy, education and poverty and social exclusion) [ 8 ], and we consider the targets addressed by objectives defined in the specific call that funds the research project.

This understanding of the social impact of research is connected to the creation of the Social Impact Open Repository (SIOR), which constitutes the first open repository worldwide that displays, cites and stores the social impact of research results [ 9 ]. The SIOR has linked to ORCID and Wikipedia to allow the synergies of spreading information about the social impact of research through diverse channels and audiences. It is relevant to mention that currently, SIOR includes evidence of real social impact, which implies that the research results have led to actual improvements in society. However, it is common to find evidence of potential social impact in research projects. The potential social impact implies that in the development of the research, there has been some evidence of the effectiveness of the research results in terms of social impact, but the results have not yet been transferred.

Additionally, a common confusion is found among the uses of dissemination, transference (policy impact) and social impact. While dissemination means to disseminate the knowledge created by research to citizens, companies and institutions, transference refers to the use of this knowledge by these different actors (or others), and finally, as already mentioned, social impact refers to the actual improvements resulting from the use of this knowledge in relation to the goals motivating the research project (such as the United Nations Sustainable Development Goals). In the present research [ 3 ], it is argued that “social impact can be understood as the culmination of the prior three stages of the research” (p.3). Therefore, this study builds on previous contributions measuring the dissemination and transference of research and goes beyond to propose a novel methodological approach to track social impact evidences.

In fact, the contribution that we develop in this article is based on the creation of a new method to evaluate the evidence of social impact shared in social media. The evaluation proposed is to measure the social impact coverage ratio (SICOR), focusing on the presence of evidence of social impact shared in social media. Then, the article first presents some of the contributions from the literature review focused on the research on social media as a source for obtaining key data for monitoring or evaluating different research purposes. Second, the SISM (social impact through social media) methodology[ 10 ] developed is introduced in detail. This methodology identifies quantitative and qualitative evidence of the social impact of the research shared on social media, specifically on Twitter and Facebook, and defines the SICOR, the social impact coverage ratio. Next, the results are discussed, and lastly, the main conclusions and further steps are presented.

Literature review

Social media research includes the analysis of citizens’ voices on a wide range of topics [ 11 ]. According to quantitative data from April 2017 published by Statista [ 12 ], Twitter and Facebook are included in the top ten leading social networks worldwide, as ranked by the number of active users. Facebook is at the top of the list, with 1,968 million active users, and Twitter ranks 10 th , with 319 million active users. Between them are the following social networks: WhatsApp, YouTube, Facebook Messenger, WeChat, QQ, Instagram,Qzone and Tumblr. If we look at altmetrics, the tracking of social networks for mentions of research outputs includes Facebook, Twitter, Google+,LinkedIn, Sina Weibo and Pinterest. The social networks common to both sources are Facebook and Twitter. These are also popular platforms that have a relevant coverage of scientific content and easy access to data, and therefore, the research projects selected here for application of the SISM methodology were chosen on these platforms.

Chew and Eysenbach [ 13 ] studied the presence of selected keywords in Twitter related to public health issues, particularly during the 2009 H1N1 pandemic, identifying the potential for health authorities to use social media to respond to the concerns and needs of society. Crooks et al.[ 14 ] investigated Twitter activity in the context of a 5.8 magnitude earthquake in 2011 on the East Coast of the United States, concluding that social media content can be useful for event monitoring and can complement other sources of data to improve the understanding of people’s responses to such events. Conversations among young Canadians posted on Facebook and analysed by Martinello and Donelle [ 15 ] revealed housing and transportation as main environmental concerns, and the project FoodRisc examined the role of social media to illustrate consumers’ quick responses during food crisis situations [ 16 ]. These types of contributions illustrate that social media research implies the understanding of citizens’ concerns in different fields, including in relation to science.

Research on the synergies between science and citizens has increased over the years, according to Fresco [ 17 ], and there is a growing interest among researchers and funding agencies in how to facilitate communication channels to spread scientific results. For instance, in 1998, Lubchenco [ 18 ] advocated for a social contract that “represents a commitment on the part of all scientists to devote their energies and talents to the most pressing problems of the day, in proportion to their importance, in exchange for public funding”(p.491).

In this framework, the recent debates on how to increase the impact of research have acquired relevance in all fields of knowledge, and major developments address the methods for measuring it. As highlighted by Feng Xia et al. [ 19 ], social media constitute an emerging approach to evaluating the impact of scholarly publications, and it is relevant to consider the influence of the journal, discipline, publication year and user type. The authors revealed that people’s concerns differ by discipline and observed more interest in papers related to everyday life, biology, and earth and environmental sciences. In the field of biomedical sciences, Haustein et al. [ 20 ] analysed the dissemination of journal articles on Twitter to explore the correlations between tweets and citations and proposed a framework to evaluate social media-based metrics. In fact, different studies address the relationship between the presence of articles on social networks and citations [ 21 ]. Bornmann [ 22 ] conducted a case study using a sample of 1,082 PLOS journal articles recommended in F1000 to explore the usefulness of altmetrics for measuring the broader impact of research. The author presents evidence about Facebook and Twitter as social networks that may indicate which papers in the biomedical sciences can be of interest to broader audiences, not just to specialists in the area. One aspect of particular interest resulting from this contribution is the potential to use altmetrics to measure the broader impacts of research, including the societal impact. However, most of the studies investigating social or societal impact lack a conceptualization underlying its measurement.

To the best of our knowledge, the assessment of social impact in social media (SISM) has developed according to this gap. At the core of this study, we present and discuss the results obtained through the application of the SICOR (social impact coverage ratio) with examples of evidence of social impact shared in social media, particularly on Twitter and Facebook, and the implications for further research.

Following these previous contributions, our research questions were as follows: Is there evidence of social impact of research shared by citizens in social media? If so, is there quantitative or qualitative evidence? How can social media contribute to identifying the social impact of research?

Methods and data presentation

A group of new methodologies related to the analysis of online data has recently emerged. One of these emerging methodologies is social media analytics [ 23 ], which was initially used most in the marketing research field but also came to be used in other domains due to the multiple possibilities opened up by the availability and richness of the data for different research purposes. Likewise, the concern of how to evaluate the social impact of research as well as the development of methodologies for addressing this concern has occupied central attention. The development of SISM (Social Impact in Social Media) and the application of the SICOR (Social Impact Coverage Ratio) is a contribution to advancement in the evaluation of the social impact of research through the analysis of the social media selected (in this case, Twitter and Facebook). Thus, SISM is novel in both social media analytics and among the methodologies used to evaluate the social impact of research. This development has been made under IMPACT-EV, a research project funded under the Framework Program FP7 of the Directorate-General for Research and Innovation of the European Commission. The main difference from other methodologies for measuring the social impact of research is the disentanglement between dissemination and social impact. While altmetrics is aimed at measuring research results disseminated beyond academic and specialized spheres, SISM contribute to advancing this measurement by shedding light on to what extent evidence of the social impact of research is found in social media data. This involves the need to differentiate between tweets or Facebook posts (Fb/posts) used to disseminate research findings from those used to share the social impact of research. We focus on the latter, investigating whether there is evidence of social impact, including both potential and real social impact. In fact, the question is whether research contributes and/or has the potential to contribute to improve the society or living conditions considering one of these goals defined. What is the evidence? Next, we detail the application of the methodology.

Data collection

To develop this study, the first step was to select research projects with social media data to be analysed. The selection of research projects for application of the SISM methodology was performed according to three criteria.

Criteria 1. Selection of success projects in FP7. The projects were success stories of the 7 th Framework Programme (FP7) highlighted by the European Commission [ 24 ] in the fields of knowledge of medicine, public health, biology and genomics. The FP7 published calls for project proposals from 2007 to 2013. This implies that most of the projects funded in the last period of the FP7 (2012 and 2013) are finalized or in the last phase of implementation.

Criteria 2. Period of implementation. We selected projects in the 2012–2013 period because they combine recent research results with higher possibilities of having Twitter and Facebook accounts compared with projects of previous years, as the presence of social accounts in research increased over this period.

Criteria 3. Twitter and Facebook accounts. It was crucial that the selected projects had active Twitter and Facebook accounts.

Table 1 summarizes the criteria and the final number of projects identified. As shown, 10 projects met the defined criteria. Projects in medical research and public health had higher presence.

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https://doi.org/10.1371/journal.pone.0203117.t001

After the selection of projects, we defined the timeframe of social media data extraction on Twitter and Facebook from the starting date of the project until the day of the search, as presented in Table 2 .

https://doi.org/10.1371/journal.pone.0203117.t002

The second step was to define the search strategies for extracting social media data related to the research projects selected. In this line, we defined three search strategies.

Strategy 1. To extract messages published on the Twitter account and the Facebook page of the selected projects. We listed the Twitter accounts and Facebook pages related to each project in order to look at the available information. In this case, it is important to clarify that the tweets published under the corresponding Twitter project account are original tweets or retweets made from this account. It is relevant to mention that in one case, the Twitter account and Facebook page were linked to the website of the research group leading the project. In this case, we selected tweets and Facebook posts related to the project. For instance, in the case of the Twitter account, the research group created a specific hashtag to publish messages related to the project; therefore, we selected only the tweets published under this hashtag. In the analysis, we prioritized the analysis of the tweets and Facebook posts that received some type of interaction (likes, retweets or shares) because such interaction is a proxy for citizens’ interest. In doing so, we used the R program and NVivoto extract the data and proceed with the analysis. Once we obtained the data from Twitter and Facebook, we were able to have an overview of the information to be further analysed, as shown in Table 3 .

https://doi.org/10.1371/journal.pone.0203117.t003

We focused the second and third strategies on Twitter data. In both strategies, we extracted Twitter data directly from the Twitter Advanced Search tool, as the API connected to NVivo and the R program covers only a specific period of time limited to 7/9 days. Therefore, the use of the Twitter Advanced Search tool made it possible to obtain historic data without a period limitation. We downloaded the results in PDF and then uploaded them to NVivo.

Strategy 2. To use the project acronym combined with other keywords, such as FP7 or EU. This strategy made it possible to obtain tweets mentioning the project. Table 4 presents the number of tweets obtained with this strategy.

https://doi.org/10.1371/journal.pone.0203117.t004

Strategy 3. To use searchable research results of projects to obtain Twitter data. We defined a list of research results, one for each project, and converted them into keywords. We selected one searchable keyword for each project from its website or other relevant sources, for instance, the brief presentations prepared by the European Commission and published in CORDIS. Once we had the searchable research results, we used the Twitter Advanced Search tool to obtain tweets, as presented in Table 5 .

https://doi.org/10.1371/journal.pone.0203117.t005

The sum of the data obtained from these three strategies allowed us to obtain a total of 3,425 tweets and 1,925 posts on public Facebook pages. Table 6 presents a summary of the results.

https://doi.org/10.1371/journal.pone.0203117.t006

We imported the data obtained from the three search strategies into NVivo to analyse. Next, we select tweets and Facebook posts providing linkages with quantitative or qualitative evidence of social impact, and we complied with the terms of service for the social media from which the data were collected. By quantitative and qualitative evidence, we mean data or information that shows how the implementation of research results has led to improvements towards the fulfilment of the objectives defined in the EU2020 strategy of the European Commission or other official targets. For instance, in the case of quantitative evidence, we searched tweets and Facebook posts providing linkages with quantitative information about improvements obtained through the implementation of the research results of the project. In relation to qualitative evidence, for example, we searched for testimonies that show a positive evaluation of the improvement due to the implementation of research results. In relation to this step, it is important to highlight that social media users are intermediaries making visible evidence of social impact. Users often share evidence, sometimes sharing a link to an external resource (e.g., a video, an official report, a scientific article, news published on media). We identified evidence of social impact in these sources.

Data analysis

γ i is the total number of messages obtained about project i with evidence of social impact on social media platforms (Twitter, Facebook, Instagram, etc.);

T i is the total number of messages from project i on social media platforms (Twitter, Facebook, Instagram, etc.); and

n is the number of projects selected.

Analytical categories and codebook

The researchers who carried out the analysis of the social media data collected are specialists in the social impact of research and research on social media. Before conducting the full analysis, two aspects were guaranteed. First, how to identify evidence of social impact relating to the targets defined by the EU2020 strategy or to specific goals defined by the call addressed was clarified. Second, we held a pilot to test the methodology with one research project that we know has led to considerable social impact, which allowed us to clarify whether or not it was possible to detect evidence of social impact shared in social media. Once the pilot showed positive results, the next step was to extend the analysis to another set of projects and finally to the whole sample. The construction of the analytical categories was defined a priori, revised accordingly and lastly applied to the full sample.

Different observations should be made. First, in this previous analysis, we found that the tweets and Facebook users play a key role as “intermediaries,” serving as bridges between the larger public and the evidence of social impact. Social media users usually share a quote or paragraph introducing evidence of social impact and/or link to an external resource, for instance, a video, official report, scientific article, news story published on media, etc., where evidence of the social impact is available. This fact has implications for our study, as our unit of analysis is all the information included in the tweets or Facebook posts. This means that our analysis reaches the external resources linked to find evidence of social impact, and for this reason, we defined tweets or Facebook posts providing linkages with information about social impact.

Second, the other important aspect is the analysis of the users’ profile descriptions, which requires much more development in future research given the existing limitations. For instance, some profiles are users’ restricted due to privacy reasons, so the information is not available; other accounts have only the name of the user with no description of their profile available. Therefore, we gave priority to the identification of evidence of social impact including whether a post obtained interaction (retweets, likes or shares) or was published on accounts other than that of the research project itself. In the case of the profile analysis, we added only an exploratory preliminary result because this requires further development. Considering all these previous details, the codebook (see Table 7 ) that we present as follows is a result of this previous research.

https://doi.org/10.1371/journal.pone.0203117.t007

How to analyse Twitter and Facebook data

To illustrate how we analysed data from Twitter and Facebook, we provide one example of each type of evidence of social impact defined, considering both real and potential social impact, with the type of interaction obtained and the profiles of those who have interacted.

QUANESISM. Tweet by ZeroHunger Challenge @ZeroHunger published on 3 May 2016. Text: How re-using food waste for animal feed cuts carbon emissions.-NOSHAN project hubs.ly/H02SmrP0. 7 retweets and 5 likes.

The unit of analysis is all the content of the tweet, including the external link. If we limited our analysis to the tweet itself, it would not be evidence. Examining the external link is necessary to find whether there is evidence of social impact. The aim of this project was to investigate the process and technologies needed to use food waste for feed production at low cost, with low energy consumption and with a maximal evaluation of the starting wastes. This tweet provides a link to news published in the PHYS.org portal [ 25 ], which specializes in science news. The news story includes an interview with the main researcher that provides the following quotation with quantitative evidence:

'Our results demonstrated that with a NOSHAN 10 percent mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions were reduced by 0.3 kg compared to a non-food waste diet,' explains Montse Jorba, NOSHAN project coordinator. 'If 1 percent of total chicken broiler feed in Europe was switched to the 10 percent NOSHAN mix diet, the total amount of CO2 emissions avoided would be 0.62 million tons each year.'[ 25 ]

This quantitative evidence “a NOSHAN 10 percent mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions carbon dioxide emissions were reduced by 0.3 kg to a non-food waste diet” is linked directly with the Europe 2020 target of Climate Change & Energy, specifically with the target of reducing greenhouse gas emissions by 20% compared to the levels in 1990 [ 8 ]. The illustrative extrapolation the coordinator mentioned in the news is also an example of quantitative evidence, although is an extrapolation based on the specific research result.

This tweet was captured by the Acronym search strategy. It is a message tweeted by an account that is not related to the research project. The twitter account is that of the Zero Hunger Challenge movement, which supports the goals of the UN. The interaction obtained is 7 retweets and 5 likes. Regarding the profiles of those who retweeted and clicked “like”, there were activists, a journalist, an eco-friendly citizen, a global news service, restricted profiles (no information is available on those who have retweeted) and one account with no information in its profile.

The following example illustrates the analysis of QUALESISM: Tweet by @eurofitFP7 published on4 October 2016. Text: See our great new EuroFIT video on youtube! https://t.co/TocQwMiW3c 9 retweets and 5 likes.

The aim of this project is to improve health through the implementation of two novel technologies to achieve a healthier lifestyle. The tweet provides a link to a video on YouTube on the project’s results. In this video, we found qualitative evidence from people who tested the EuroFit programme; there are quotes from men who said that they have experienced improved health results using this method and that they are more aware of how to manage their health:

One end-user said: I have really amazing results from the start, because I managed to change a lot of things in my life. And other one: I was more conscious of what I ate, I was more conscious of taking more steps throughout the day and also standing up a little more. [ 26 ]

The research applies the well researched scientific evidence to the management of health issues in daily life. The video presents the research but also includes a section where end-users talk about the health improvements they experienced. The quotes extracted are some examples of the testimonies collected. All agree that they have improved their health and learned healthy habits for their daily lives. These are examples of qualitative evidence linked with the target of the call HEALTH.2013.3.3–1—Social innovation for health promotion [ 27 ] that has the objectives of reducing sedentary habits in the population and promoting healthy habits. This research contributes to this target, as we see in the video testimonies. Regarding the interaction obtained, this tweet achieved 9 retweets and 5 likes. In this case, the profiles of the interacting citizens show involvement in sport issues, including sport trainers, sport enthusiasts and some researchers.

To summarize the analysis, in Table 8 below, we provide a summary with examples illustrating the evidence found.

https://doi.org/10.1371/journal.pone.0203117.t008

Quantitative evidence of social impact in social media

There is a greater presence of tweets/Fb posts with quantitative evidence (14) than with qualitative evidence (9) in the total number of tweets/Fb posts identified with evidence of social impact. Most of the tweets/Fb posts with quantitative evidence of social impact are from scientific articles published in peer-reviewed international journals and show potential social impact. In Table 8 , we introduce 3 examples of this type of tweets/Fb posts with quantitative evidence:

The first tweet with quantitative social impact selected is from project 7. The aim of this project was to provide high-quality scientific evidence for preventing vitamin D deficiency in European citizens. The tweet highlighted the main contribution of the published study, that is, “Weekly consumption of 7 vitamin D-enhanced eggs has an important impact on winter vitamin D status in adults” [ 28 ]. The quantitative evidence shared in social media was extracted from a news publication in a blog on health news. This blog collects scientific articles of research results. In this case, the blog disseminated the research result focused on how vitamin D-enhanced eggs improve vitamin D deficiency in wintertime, with the published results obtained by the research team of the project selected. The quantitative evidence illustrates that the group of adults who consumed vitamin D-enhanced eggs did not suffer from vitamin D deficiency, as opposed to the control group, which showed a significant decrease in vitamin D over the winter. The specific evidence is the following extracted from the article [ 28 ]:

With the use of a within-group analysis, it was shown that, although serum 25(OH) D in the control group significantly decreased over winter (mean ± SD: -6.4 ± 6.7 nmol/L; P = 0.001), there was no change in the 2 groups who consumed vitamin D-enhanced eggs (P>0.1 for both. (p. 629)

This evidence contributes to achievement of the target defined in the call addressed that is KBBE.2013.2.2–03—Food-based solutions for the eradication of vitamin D deficiency and health promotion throughout the life cycle [ 29 ]. The quantitative evidence shows how the consumption of vitamin D-enhanced eggs reduces vitamin D deficiency.

The second example of this table corresponds to the example of quantitative evidence of social impact provided in the previous section.

The third example is a Facebook post from project 3 that is also tweeted. Therefore, this evidence was published in both social media sources analysed. The aim of this project was to measure a range of chemical and physical environmental hazards in food, consumer products, water, air, noise, and the built environment in the pre- and postnatal early-life periods. This Facebook post and tweet links directly to a scientific article [ 30 ] that shows the precision of the spectroscopic platform:

Using 1H NMR spectroscopy we characterized short-term variability in urinary metabolites measured from 20 children aged 8–9 years old. Daily spot morning, night-time and pooled (50:50 morning and night-time) urine samples across six days (18 samples per child) were analysed, and 44 metabolites quantified. Intraclass correlation coefficients (ICC) and mixed effect models were applied to assess the reproducibility and biological variance of metabolic phenotypes. Excellent analytical reproducibility and precision was demonstrated for the 1H NMR spectroscopic platform (median CV 7.2%) . (p.1)

This evidence is linked to the target defined in the call “ENV.2012.6.4–3—Integrating environmental and health data to advance knowledge of the role of environment in human health and well-being in support of a European exposome initiative” [ 31 ]. The evidence provided shows how the project’s results have contributed to building technology for improving the data collection to advance in the knowledge of the role of the environment in human health, especially in early life. The interaction obtained is one retweet from a citizen from Nigeria interested in health issues, according to the information available in his profile.

Qualitative evidence of social impact in social media

We found qualitative evidence of the social impact of different projects, as shown in Table 9 . Similarly to the quantitative evidence, the qualitative cases also demonstrate potential social impact. The three examples provided have in common that they are tweets or Facebook posts that link to videos where the end users of the research project explain their improvements once they have implemented the research results.

https://doi.org/10.1371/journal.pone.0203117.t009

The first tweet with qualitative evidence selected is from project 4. The aim of this project is to produce a system that helps in the prevention of obesity and eating disorders, targeting young people and adults [ 32 ]. The twitter account that published this tweet is that of the Future and Emerging Technologies Programme of the European Commission, and a link to a Euronews video is provided. This video shows how the patients using the technology developed in the research achieved control of their eating disorders, through the testimonies of patients commenting on the positive results they have obtained. These testimonies are included in the news article that complements the video. An example of these testimonies is as follows:

Pierre Vial has lost 43 kilos over the past nine and a half months. He and other patients at the eating disorder clinic explain the effects obesity and anorexia have had on their lives. Another patient, Karin Borell, still has some months to go at the clinic but, after decades of battling anorexia, is beginning to be able to visualise life without the illness: “On a good day I see myself living a normal life without an eating disorder, without problems with food. That’s really all I wish right now”.[ 32 ]

This qualitative evidence shows how the research results contribute to the achievement of the target goals of the call addressed:“ICT-2013.5.1—Personalised health, active ageing, and independent living”. [ 33 ] In this case, the results are robust, particularly for people suffering chronic diseases and desiring to improve their health; people who have applied the research findings are improving their eating disorders and better managing their health. The value of this evidence is the inclusion of the patients’ voices stating the impact of the research results on their health.

The second example is a Facebook post from project 9, which provides a link to a Euronews video. The aim of this project is to bring some tools from the lab to the farm in order to guarantee a better management of the farm and animal welfare. In this video [ 34 ], there are quotes from farmers using the new system developed through the research results of the project. These quotes show how use of the new system is improving the management of the farm and the health of the animals; some examples are provided:

Cameras and microphones help me detect in real time when the animals are stressed for whatever reason,” explained farmer Twan Colberts. “So I can find solutions faster and in more efficient ways, without me being constantly here, checking each animal.”

This evidence shows how the research results contribute to addressing the objectives specified in the call “KBBE.2012.1.1–02—Animal and farm-centric approach to precision livestock farming in Europe” [ 29 ], particularly, to improve the precision of livestock farming in Europe. The interaction obtained is composed of6 likes and 1 share. The profiles are diverse, but some of them do not disclose personal information; others have not added a profile description, and only their name and photo are available.

Interrater reliability (kappa)

The analysis of tweets and Facebook posts providing linkages with information about social impact was conducted following a content analysis method in which reliability was based on a peer review process. This sample is composed of 3,425 tweets and 1,925 Fb/posts. Each tweet and Facebook post was analysed to identify whether or not it contains evidence of social impact. Each researcher has the codebook a priori. We used interrater reliability in examining the agreement between the two raters on the assignment of the categories defined through Cohen’s kappa. We used SPSS to calculate this coefficient. We exported an excel sheet with the sample coded by the two researchers being 1 (is evidence of social impact, either potential or real) and 0 (is not evidence of social impact) to SPSS. The cases where agreement was not achieved were not considered as containing evidence of social impact. The result obtained is 0.979; considering the interpretation of this number according to Landis & Koch [ 35 ], our level of agreement is almost perfect, and thus, our analysis is reliable. To sum up the data analysis, the description of the steps followed is explained:

Step 1. Data analysis I. We included all data collected in an excel sheet to proceed with the analysis. Prior to the analysis, researchers read the codebook to keep in mind the information that should be identified.

Step 2. Each researcher involved reviewed case by case the tweets and Facebook posts to identify whether they provide links with evidence of social impact or not. If the researcher considers there to be evidence of social impact, he or she introduces the value of 1into the column, and if not, the value of 0.

Step 3. Once all the researchers have finished this step, the next step is to export the excel sheet to SPSS to extract the kappa coefficient.

Step 4. Data Analysis II. The following step was to analyse case by case the tweets and Facebook posts identified as providing linkages with information of social impact and classify them as quantitative or qualitative evidence of social impact.

Step 5. The interaction received was analysed because this determines to which extent this evidence of social impact has captured the attention of citizens (in the form of how many likes, shares, or retweets the post has).

Step 6. Finally, if available, the profile descriptions of the citizens interacting through retweeting or sharing the Facebook post were considered.

Step 7. SICOR was calculated. It could be applied to the complete sample (all data projects) or to each project, as we will see in the next section.

The total number of tweets and Fb/posts collected from the 10 projects is 5,350. After the content analysis, we identified 23 tweets and Facebook posts providing linkages to information about social impact. To respond to the research question, which considered whether there is evidence of social impact shared by citizens in social media, the answer was affirmative, although the coverage ratio is low. Both Twitter and Facebook users retweeted or shared evidence of social impact, and therefore, these two social media networks are valid sources for expanding knowledge on the assessment of social impact. Table 10 shows the social impact coverage ratio in relation to the total number of messages analysed.

https://doi.org/10.1371/journal.pone.0203117.t010

The analysis of each of the projects selected revealed some results to consider. Of the 10 projects, 7 had evidence, but those projects did not necessarily have more Tweets and Facebook posts. In fact, some projects with fewer than 70 tweets and 50 Facebook posts have more evidence of social impact than other projects with more than 400 tweets and 400 Facebook posts. This result indicates that the number of tweets and Facebook posts does not determine the existence of evidence of social impact in social media. For example, project 2 has 403 tweets and 423 Facebooks posts, but it has no evidence of social impact on social media. In contrast, project 9 has 62 tweets, 43 Facebook posts, and 2 pieces of evidence of social impact in social media, as shown in Table 11 .

https://doi.org/10.1371/journal.pone.0203117.t011

The ratio of tweets/Fb posts to evidence is 0.43%, and it differs depending on the project, as shown below in Table 12 . There is one project (P7) with a ratio of 4.98%, which is a social impact coverage ratio higher than that of the other projects. Next, a group of projects (P3, P9, P10) has a social impact coverage ratio between 1.41% and 2,99%.The next slot has three projects (P1, P4, P5), with a ratio between 0.13% and 0.46%. Finally, there are three projects (P2, P6, P8) without any tweets/Fb posts evidence of social impact.

https://doi.org/10.1371/journal.pone.0203117.t012

Considering the three strategies for obtaining data, each is related differently to the evidence of social impact. In terms of the social impact coverage ratio, as shown in Table 13 , the most successful strategy is number 3 (searchable research results), as it has a relation of 17.86%, which is much higher than the ratios for the other 2 strategies. The second strategy (acronym search) is more effective than the first (profile accounts),with 1.77% for the former as opposed to 0.27% for the latter.

https://doi.org/10.1371/journal.pone.0203117.t013

Once tweets and Facebook posts providing linkages with information about social impact(ESISM)were identified, we classified them in terms of quantitative (QUANESISM) or qualitative evidence (QUALESISM)to determine which type of evidence was shared in social media. Table 14 indicates the amount of quantitative and qualitative evidence identified for each search strategy.

https://doi.org/10.1371/journal.pone.0203117.t014

First, the results obtained indicated that the SISM methodology aids in calculating the social impact coverage ratio of the research projects selected and evaluating whether the social impact of the corresponding research is shared by citizens in social media. The social impact coverage ratio applied to the sample selected is low, but when we analyse the SICOR of each project separately, we can observe that some projects have a higher social impact coverage ratio than others. Complementary to altmetrics measuring the extent to which research results reach out society, the SICOR considers the question whether this process includes evidence of potential or real social impact. In this sense, the overall methodology of SISM contributes to advancement in the evaluation of the social impact of research by providing a more precise approach to what we are evaluating.

This contribution complements current evaluation methodologies of social impact that consider which improvements are shared by citizens in social media. Exploring the results in more depth, it is relevant to highlight that of the ten projects selected, there is one research project with a social impact coverage ratio higher than those of the others, which include projects without any tweets or Facebook posts with evidence of social impact. This project has a higher ratio of evidence than the others because evidence of its social impact is shared more than is that of other projects. This also means that the researchers produced evidence of social impact and shared it during the project. Another relevant result is that the quantity of tweets and Fb/posts collected did not determine the number of tweets and Fb/posts found with evidence of social impact. Moreover, the analysis of the research projects selected showed that there are projects with less social media interaction but with more tweets and Fb/posts containing evidence of social media impact. Thus, the number of tweets and Fb/posts with evidence of social impact is not determined by the number of publication messages collected; it is determined by the type of messages published and shared, that is, whether they contain evidence of social impact or not.

The second main finding is related to the effectiveness of the search strategies defined. Related to the strategies carried out under this methodology, one of the results found is that the most effective search strategy is the searchable research results, which reveals a higher percentage of evidence of social impact than the own account and acronym search strategies. However, the use of these three search strategies is highly recommended because the combination of all of them makes it possible to identify more tweets and Facebook posts with evidence of social impact.

Another result is related to the type of evidence of social impact found. There is both quantitative and qualitative evidence. Both types are useful for understanding the type of social impact achieved by the corresponding research project. In this sense, quantitative evidence allows us to understand the improvements obtained by the implementation of the research results and capture their impact. In contrast, qualitative evidence allows us to deeply understand how the resultant improvements obtained from the implementation of the research results are evaluated by the end users by capturing their corresponding direct quotes. The social impact includes the identification of both real and potential social impact.

Conclusions

After discussing the main results obtained, we conclude with the following points. Our study indicates that there is incipient evidence of social impact, both potential and real, in social media. This demonstrates that researchers from different fields, in the present case involved in medical research, public health, animal welfare and genomics, are sharing the improvements generated by their research and opening up new venues for citizens to interact with their work. This would imply that scientists are promoting not only the dissemination of their research results but also the evidence on how their results may lead to the improvement of societies. Considering the increasing relevance and presence of the dissemination of research, the results indicate that scientists still need to include in their dissemination and communication strategies the aim of sharing the social impact of their results. This implies the publication of concrete qualitative or quantitative evidence of the social impact obtained. Because of the inclusion of this strategy, citizens will pay more attention to the content published in social media because they are interested in knowing how science can contribute to improving their living conditions and in accessing crucial information. Sharing social impact in social media facilitates access to citizens of different ages, genders, cultural backgrounds and education levels. However, what is most relevant for our argument here is how citizens should also be able to participate in the evaluation of the social impact of research, with social media a great source to reinforce this democratization process. This contributes not only to greatly improving the social impact assessment, as in addition to experts, policy makers and scientific publications, citizens through social media contribute to making this assessment much more accurate. Thus, citizens’ contribution to the dissemination of evidence of the social impact of research yields access to more diverse sectors of society and information that might be unknown by the research or political community. Two future steps are opened here. On the one hand, it is necessary to further examine the profiles of users who interact with this evidence of social impact considering the limitations of the privacy and availability of profile information. A second future task is to advance in the articulation of the role played by citizens’ participation in social impact assessment, as citizens can contribute to current worldwide efforts by shedding new light on this process of social impact assessment and contributing to making science more relevant and useful for the most urgent and poignant social needs.

Supporting information

S1 file. interrater reliability (kappa) result..

This file contains the SPSS file with the result of the calculation of Cohen’s Kappa regards the interrater reliability. The word document exported with the obtained result is also included.

https://doi.org/10.1371/journal.pone.0203117.s001

S2 File. Data collected and SICOR calculation.

This excel contains four sheets, the first one titled “data collected” contains the number of tweets and Facebook posts collected through the three defined search strategies; the second sheet titled “sample” contains the sample classified by project indicating the ID of the message or code assigned, the type of message (tweet or Facebook post) and the codification done by researchers being 1 (is evidence of social impact, either potential or real) and 0 (is not evidence of social impact); the third sheet titled “evidence found” contains the number of type of evidences of social impact founded by project (ESISM-QUANESIM or ESISM-QUALESIM), search strategy and type of message (tweet or Facebook posts); and the last sheet titled “SICOR” contains the Social Impact Coverage Ratio calculation by projects in one table and type of search strategy done in another one.

https://doi.org/10.1371/journal.pone.0203117.s002

Acknowledgments

The research leading to these results received funding from the 7 th Framework Programme of the European Commission under Grant Agreement n° 613202. The extraction of available data using the list of searchable keywords on Twitter and Facebook followed the ethical guidelines for social media research supported by the Economic and Social Research Council (UK) [ 36 ] and the University of Aberdeen [ 37 ]. Furthermore, the research results have already been published and made public, and hence, there are no ethical issues.

View Article
PubMed/NCBI
Google Scholar
6. Godin B, Dore C. Measuring the impacts of science; beyond the economic dimension. INRS Urbanisation, Culture et Sociult, HSIT Lecture. 2005. Helsinki, Finland: Helsinki Institute for Science and Technology Studies.
7. European Commission. Better regulation Toolbox. 2017: 129–130. Available from https://ec.europa.eu/info/sites/info/files/better-regulation-toolbox_0.pdf
8. European 2020 Strategy. 2010. Available from https://ec.europa.eu/info/business-economy-euro/economic-and-fiscal-policy-coordination/eu-economic-governance-monitoring-prevention-correction/european-semester/framework/europe-2020-strategy_en
10. Flecha R, Sordé-Martí T. SISM Methodology (Social Impact through Social Media).2016. Available from https://archive.org/details/SISMMethodology
12. Statista. Statistics and facts about social media usage. 2017. Available from https://www.statista.com/topics/1164/social-networks/
16. FoodRisc Consortium, European Commission, Main researcher: Patrick Wall. FoodRisc—Result in Brief (Seventh Framework Programme). 2017. Available from http://cordis.europa.eu/result/rcn/90678_en.html
23. Murphy, L. Grit Report. Greenbook research industry trends report. Grit Q3-Q4 2016; (Vol. 3–4). New York. Retrieved from https://www.greenbook.org/grit
24. EC Research & Innovation. Success Stories [Internet]. 2017 [cited 2017 Apr 25]. Available from: http://ec.europa.eu/research/infocentre/success_stories_en.cfm
25. PhysOrg. How re-using food waste for animal feed cuts carbon emissions. 2016. Available from: https://phys.org/news/2016-04-re-using-food-animal-carbon-emissions.html
26. EuroFIT. EuroFIT International PP Youtube. 2016; minute 2.05–2.20. Available from: https://www.youtube.com/watch?time_continue=155&v=CHkbnD8IgZw
27. European Commission<. Work Programme 2013. Cooperation Theme 1 Health. (2013). Available from: https://ec.europa.eu/research/participants/portal/doc/call/fp7/common/1567645-1._health_upd_2013_wp_27_june_2013_en.pdf
29. European Commission. Work Programme 2013. Cooperation Theme 2. Food, agriculture, and fisheries, and biotechnology. (2013). Available from: http://ec.europa.eu/research/participants/data/ref/fp7/192042/b-wp-201302_en.pdf
31. European Commission. Work Programme 2012. Cooperation Theme 6 Environment (including climate change). (2011). Available from: http://ec.europa.eu/research/participants/data/ref/fp7/89467/f-wp-201201_en.pdf
32. Research Information Center. Technology trialled in fight against ticking timebomb of obesity. 2016. Available from: http://www.euronews.com/2016/08/10/technology-trialled-in-fight-against-ticking-timebomb-of-obesity
33. European Commission. Work Programme 2013. Cooperation Theme 3. ICT (Information and Communication Technologies) (2012). Available from: http://ec.europa.eu/research/participants/data/ref/fp7/132099/c-wp-201301_en.pdf
34. Euronews. Big farmer is watching! Surveillance technology monitors animal wellbeing. 2016. Available from http://www.euronews.com/2016/05/09/big-farmer-is-watching-surveillance-technology-monitors-animal-wellbeing
36. Economic and Social Research Council. Social Media Best Practice and Guidance. Using Social Media. 2017. Available from: http://www.esrc.ac.uk/research/impact-toolkit/social-media/using-social-media/
37. Townsend, L. & Wallace, C. Social Media Research: A Guide to Ethics. 2016. Available from http://www.gla.ac.uk/media/media_487729_en.pdf

An Evidence-based Perspective on Social Media Use Among Young People

Social media is fully ingrained in young people’s everyday lives, shaping how they connect, communicate, learn, and interact with the world. As social media has proliferated in young people’s lives, so have concerns about its role in the decline of youth mental health. Although concerns about the adverse effects of social media on young people are widespread, it is crucial to acknowledge the benefits it can offer as well.

The third installment of Hopelab’s National Survey, “ A Double-Edged Sword: How Diverse Communities of Young People Think About the Multifaceted Relationship Between Social Media and Mental Health, ” released in partnership with Common Sense Media, explores the nuances of the relationship between social media and youth mental health. Importantly, this survey was co-created with young people, who provided direction and input regarding survey content and worked with the study team to prioritize and interpret results. Teens and young adults — especially BIPOC and LGBTQ+ young people — bring their own unique lived experiences to social media spaces and, as a result, have different experiences with the content and communities they find there.

Many conversations surrounding social media and young people focus solely on harm and characterize them as passive users. This research shows that young people’s relationship with social media is much more complex. Social media is an important source of connection, support, and affirmation for young people, but it also brings challenges that young people navigate to minimize harmful impacts. There is an opportunity to use and understand this data to create better solutions grounded in the realities of young people’s experiences and needs.

Young friends watching social media content on smartphones

Evidence of ways social media may differentially support youth well-being

Research indicates that social media has a dual nature for young people, with both positive and negative implications. There is also great diversity in the ways social media impacts young people. As stated in the American Psychological Association’s Health Advisory on Social Media Use in Adolescence, “Social media is not inherently beneficial or harmful to young people…the effects of social media likely depend on what teens can do and see online, teens’ preexisting strengths or vulnerabilities, and the contexts in which they grow up.” Efforts to mitigate the harmful effects of social media must take into account the wide range of benefits it can offer young people and consider differential impacts across identities.

Social Connection: Young people who are Black, LGBTQ+, or suffer from depressive symptoms are among those who ascribe even greater importance to the role of social media in helping connect with family and feel less alone. For example, relative to white and Latinx peers, Black young people more often point to the importance of social media for connecting with family (72% Black vs. 62% Latinx and 61% white). Notably, when compared with their white (53%) and Latinx (60%) peers, Black (77%) young people find social media more important for connecting with people who share similar concerns about their mental health or well-being. Further, LGBTQ+ young people (74% LGBTQ+ vs. 52% straight and cisgender) and those with moderate to severe depression symptoms (66% moderate to severe vs. 48% none) are significantly more likely than their peers to say social media is important for feeling less alone.
Information Access: Young people who are Black (64%), Latinx (59%), or have moderate to severe depression symptoms (57%) are more likely to endorse the importance of social media for finding information or resources about mental health or well-being than their white peers (44%) and those without depressive symptoms (47%). Black and Latinx young people are also more likely to say that social media platforms are essential for learning about professional or academic opportunities compared to their white peers (80% Black vs. 63% Latinx vs. 53% white).
Identity Affirmation: Almost 9 in 10 (89%) LGBTQ+ young people at least sometimes come across comments celebrating LGBTQ+ identities on social media. Further, a little over half (52%) of LGBTQ+ young people indicate a preference for communicating over social media rather than in person, compared to less than 4 in 10 straight and cisgender young people (38%). In subsequent interviews and focus groups, LGBTQ+ young people said that in the current climate of increased restrictions and hate toward those who are trans and queer, online communication often felt safer and more supportive.

Evidence of ways social media may exacerbate harm among diverse subgroups of young people

Although social media is often an essential source of social connection, information, and affirmation for young people, particularly those who are LGBTQ+, BIPOC, or experiencing symptoms of depression, it also comes with potential harm to these same groups. The lack of transparency from technology companies has prevented a complete understanding of the magnitude of social media’s impact on young people’s mental health and well-being. In May 2023 , U.S. Surgeon General Vivek Murthy advised that companies needed to take “immediate action to protect kids now” because the effects of social media on kids and teens were so largely unknown.

Social Comparison: Sixty-four percent of young people with moderate to severe depressive symptoms indicate that when they use social media, they feel as if others’ lives are better than theirs, compared to 38% of those with no symptoms, and 60% see or hear things on social media that make them feel bad about the way they look, compared to 25% of those with no symptoms. Similar findings emerged for LGBTQ+ social media users compared to straight and cisgender peers related to feeling like other people’s lives are better than their own (60% vs. 47%, respectively) and exposure to content that makes them feel bad about their body or appearance (55% vs. 37%, respectively).
Hateful and hurtful content : Latinx youth (56%) report more frequently encountering racist comments, as compared to their Black peers (47%). White (60%) and Latinx (59%) youth are also more likely than Black youth (53%) to encounter body-shaming comments. Three-fourths of LGBTQ+ youth, however, encounter both transphobic (75%) and homophobic comments (76%) on social media, versus only about half of non-LGBTQ+ youth (55% and 49%, respectively).
Negative News: Compared to their white peers (41%), Black youth (53%) and Latinx youth (48%) more often indicate that they feel the emotional toll of negative news consumption. Looking at LGBTQ+ respondents, fully 6 in 10 LGBTQ+ youth report experiencing the emotional impact of negative news, compared to 43% of straight and cisgender young people. Those with moderate to severe depressive symptoms (62%) are also more likely than those with no symptoms (32%) to report that they see so much bad news that they feel stressed and anxious.

Multiracial group of teenagers using their cell phones at high school.

Evidence of ways young people take actions to balance risks and benefits

The social media debate isn’t a good vs. bad issue. It’s a complex system where both harm and benefit exist. While aspects of social media can exacerbate mental health issues for young people, our survey data suggests that they are aware of the harms and are not passive users. They possess agency and take actions to maximize benefits while minimizing harm by curating their feeds, managing time spent online, and avoiding harmful content. Understanding the nuances of how young people balance the risks with the benefits of social media is the key to creating a safer, more empowering digital environment for and with young people.

Avoiding Negative Content: Nearly 9 in 10 (89%) LGBTQ+ youth who use social media say that, over the past year, they have tried to avoid content they do not like on these platforms, compared with just under three-fourths (74%) of their non-LGBTQ+ peers who use social media. Further, fully 9 in 10 young people with moderate to severe depressive symptoms have tried to see less of what they do not like on social media compared to 67% of those with no symptoms.
Curating Social Media Feeds: LGBTQ+ youth are also significantly more likely than straight and cisgender youth to have tried curating their feed (78% vs. 65%). In addition, 8 in 10 young people with moderate to severe (81%) depressive symptoms have taken actions to try to curate their social media feed, compared to just over half (55%) of those with no symptoms.
Managing Time Concerns: About three-fourths of Black (74%) and Latinx (73%) young people who use social media have taken a temporary break from an account due to concerns about spending too much time on it, compared to slightly over half of white youth (56%). Further, the majority of Black (56%) and Latinx (54%) youth chose to take a permanent break from an account in the past year for this reason, versus only about one in three white youth (32%). Young people with moderate to severe depressive symptoms were also more likely to take a temporary (76%) or permanent (56%) break from a social media account due to time-related concerns compared to those with no symptoms (51% and 32%, respectively).
Managing Harassment and Negative Experiences : Black (42%) and Latinx (40%) young people are about twice as likely as white youth (21%) to have taken a permanent break from a social media account—and more than one and a half times more likely to have taken a temporary break (48%, 47%, and 30%, respectively)—due to harassment or other negative experiences online. Additionally, young people with moderate to severe depressive symptoms are more than twice as likely to have taken a temporary (58%) and permanent break (44%) due to online harassment and other negative experiences than those with no symptoms (25% and 20%, respectively).

The actions young people are taking to balance the positives and negatives of social media speak volumes about their agency and decision-making abilities. However, it also underscores the need for platforms to address online safety and well-being issues. Our work creating opportunities for young people to thrive is far from over. We must use this research to guide our next steps, remembering that the experiences of young people – with diverse perspectives – inform the data. Progress will only be successful if we continue to center young people and partner with them to build solutions that meet their needs.

Read the complete 2024 National Survey here.

Hopelab’s Translational Science Fellowship: Join me in building a future for Black…

Dr. Charity Brown Griffin shares her personal story as a researcher and invites HBCUs researchers to apply for Hopelab’s HBCU Translational Science Fellowship

See and Be Seen: The Value of Identity in Social Media Content

Teenagers today are increasingly seeking mental health support from friends, family, digital platforms, and social media, instead of traditional resources. Hear from Gen Z podcaster, Gael Aitor about the role identity plays in social media content.

A group of four young women stand posed on steps wearing jeans and hats

Activating Intergenerational Collaboration to Improve Youth Mental Health

We’re exploring opportunities to deepen the ways we engage youth in our work and across the ecosystem to improve mental health outcomes.

light green gradient background with flock of colorful illustrated birds

When Hope Takes Flight

Exploring exciting collaborations for youth mental health and well-being.

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Open access
Published: 02 May 2024

Effectiveness of social media-assisted course on learning self-efficacy

Jiaying Hu 1 ,
Yicheng Lai 2 &
Xiuhua Yi 3

Scientific Reports volume 14 , Article number: 10112 ( 2024 ) Cite this article

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Human behaviour

The social media platform and the information dissemination revolution have changed the thinking, needs, and methods of students, bringing development opportunities and challenges to higher education. This paper introduces social media into the classroom and uses quantitative analysis to investigate the relation between design college students’ learning self-efficacy and social media for design students, aiming to determine the effectiveness of social media platforms on self-efficacy. This study is conducted on university students in design media courses and is quasi-experimental, using a randomized pre-test and post-test control group design. The study participants are 73 second-year design undergraduates. Independent samples t-tests showed that the network interaction factors of social media had a significant impact on college students learning self-efficacy. The use of social media has a significant positive predictive effect on all dimensions of learning self-efficacy. Our analysis suggests that using the advantages and value of online social platforms, weakening the disadvantages of the network, scientifically using online learning resources, and combining traditional classrooms with the Internet can improve students' learning self-efficacy.

The effectiveness of using edublogs as an instructional and motivating tool in the context of higher education

Looking back to move forward: comparison of instructors’ and undergraduates’ retrospection on the effectiveness of online learning using the nine-outcome influencing factors.

Real-world effectiveness of a social-psychological intervention translated from controlled trials to classrooms

Introduction.

Social media is a way of sharing information, ideas, and opinions with others one. It can be used to create relationships between people and businesses. Social media has changed the communication way, it’s no longer just about talking face to face but also using a digital platform such as Facebook or Twitter. Today, social media is becoming increasingly popular in everyone's lives, including students and researchers 1 . Social media provides many opportunities for learners to publish their work globally, bringing many benefits to teaching and learning. The publication of students' work online has led to a more positive attitude towards learning and increased achievement and motivation. Other studies report that student online publications or work promote reflection on personal growth and development and provide opportunities for students to imagine more clearly the purpose of their work 2 . In addition, learning environments that include student publications allow students to examine issues differently, create new connections, and ultimately form new entities that can be shared globally 3 , 4 .

Learning self-efficacy is a belief that you can learn something new. It comes from the Latin word “self” and “efficax” which means efficient or effective. Self-efficacy is based on your beliefs about yourself, how capable you are to learn something new, and your ability to use what you have learned in real-life situations. This concept was first introduced by Bandura (1977), who studied the effects of social reinforcement on children’s learning behavior. He found that when children were rewarded for their efforts they would persist longer at tasks that they did not like or had low interest in doing. Social media, a ubiquitous force in today's digital age, has revolutionized the way people interact and share information. With the rise of social media platforms, individuals now have access to a wealth of online resources that can enhance their learning capabilities. This access to information and communication has also reshaped the way students approach their studies, potentially impacting their learning self-efficacy. Understanding the role of social media in shaping students' learning self-efficacy is crucial in providing effective educational strategies that promote healthy learning and development 5 . Unfortunately, the learning curve for the associated metadata base modeling methodologies and their corresponding computer-aided software engineering (CASE) tools have made it difficult for students to grasp. Addressing this learning issue examined the effect of this MLS on the self-efficacy of learning these topics 6 . Bates et al. 7 hypothesize a mediated model in which a set of antecedent variables influenced students’ online learning self-efficacy which, in turn, affected student outcome expectations, mastery perceptions, and the hours spent per week using online learning technology to complete learning assignments for university courses. Shen et al. 8 through exploratory factor analysis identifies five dimensions of online learning self-efficacy: (a) self-efficacy to complete an online course (b) self-efficacy to interact socially with classmates (c) self-efficacy to handle tools in a Course Management System (CMS) (d) self-efficacy to interact with instructors in an online course, and (e) self-efficacy to interact with classmates for academic purposes. Chiu 9 established a model for analyzing the mediating effect that learning self-efficacy and social self-efficacy have on the relationship between university students’ perceived life stress and smartphone addiction. Kim et al. 10 study was conducted to examine the influence of learning efficacy on nursing students' self-confidence. The objective of Paciello et al. 11 was to identify self-efficacy configurations in different domains (i.e., emotional, social, and self-regulated learning) in a sample of university students using a person-centered approach. The role of university students’ various conceptions of learning in their academic self-efficacy in the domain of physics is initially explored 12 . Kumar et al. 13 investigated factors predicting students’ behavioral intentions towards the continuous use of mobile learning. Other influential work includes 14 .

Many studies have focused on social networking tools such as Facebook and MySpace 15 , 16 . Teachers are concerned that the setup and use of social media apps take up too much of their time, may have plagiarism and privacy issues, and contribute little to actual student learning outcomes; they often consider them redundant or simply not conducive to better learning outcomes 17 . Cao et al. 18 proposed that the central questions in addressing the positive and negative pitfalls of social media on teaching and learning are whether the use of social media in teaching and learning enhances educational effectiveness, and what motivates university teachers to use social media in teaching and learning. Maloney et al. 3 argued that social media can further improve the higher education teaching and learning environment, where students no longer access social media to access course information. Many studies in the past have shown that the use of modern IT in the classroom has increased over the past few years; however, it is still limited mainly to content-driven use, such as accessing course materials, so with the emergence of social media in students’ everyday lives 2 , we need to focus on developing students’ learning self-efficacy so that they can This will enable students to 'turn the tables and learn to learn on their own. Learning self-efficacy is considered an important concept that has a powerful impact on learning outcomes 19 , 20 .

Self-efficacy for learning is vital in teaching students to learn and develop healthily and increasing students' beliefs in the learning process 21 . However, previous studies on social media platforms such as Twitter and Weibo as curriculum support tools have not been further substantiated or analyzed in detail. In addition, the relationship between social media, higher education, and learning self-efficacy has not yet been fully explored by researchers in China. Our research aims to fill this gap in the topic. Our study explored the impact of social media on the learning self-efficacy of Chinese college students. Therefore, it is essential to explore the impact of teachers' use of social media to support teaching and learning on students' learning self-efficacy. Based on educational theory and methodological practice, this study designed a teaching experiment using social media to promote learning self-efficacy by posting an assignment for post-course work on online media to explore the actual impact of social media on university students’ learning self-efficacy. This study examines the impact of a social media-assisted course on university students' learning self-efficacy to explore the positive impact of a social media-assisted course.

Theoretical background

Social media

Social media has different definitions. Mayfield (2013) first introduced the concept of social media in his book-what is social media? The author summarized the six characteristics of social media: openness, participation, dialogue, communication, interaction, and communication. Mayfield 22 shows that social media is a kind of new media. Its uniqueness is that it can give users great space and freedom to participate in the communication process. Jen (2020) also suggested that the distinguishing feature of social media is that it is “aggregated”. Social media provides users with an interactive service to control their data and information and collaborate and share information 2 . Social media offers opportunities for students to build knowledge and helps them actively create and share information 23 . Millennial students are entering higher education institutions and are accustomed to accessing and using data from the Internet. These individuals go online daily for educational or recreational purposes. Social media is becoming increasingly popular in the lives of everyone, including students and researchers 1 . A previous study has shown that millennials use the Internet as their first source of information and Google as their first choice for finding educational and personal information 24 . Similarly, many institutions encourage teachers to adopt social media applications 25 . Faculty members have also embraced social media applications for personal, professional, and pedagogical purposes 17 .

Social networks allow one to create a personal profile and build various networks that connect him/her to family, friends, and other colleagues. Users use these sites to stay in touch with their friends, make plans, make new friends, or connect with someone online. Therefore, extending this concept, these sites can establish academic connections or promote cooperation and collaboration in higher education classrooms 2 . This study defines social media as an interactive community of users' information sharing and social activities built on the technology of the Internet. Because the concept of social media is broad, its connotations are consistent. Research shows that Meaning and Linking are the two key elements that make up social media existence. Users and individual media outlets generate social media content and use it as a platform to get it out there. Social media distribution is based on social relationships and has a better platform for personal information and relationship management systems. Examples of social media applications include Facebook, Twitter, MySpace, YouTube, Flickr, Skype, Wiki, blogs, Delicious, Second Life, open online course sites, SMS, online games, mobile applications, and more 18 . Ajjan and Hartshorne 2 investigated the intentions of 136 faculty members at a US university to adopt Web 2.0 technologies as tools in their courses. They found that integrating Web 2.0 technologies into the classroom learning environment effectively increased student satisfaction with the course and improved their learning and writing skills. His research focused on improving the perceived usefulness, ease of use, compatibility of Web 2.0 applications, and instructor self-efficacy. The social computing impact of formal education and training and informal learning communities suggested that learning web 2.0 helps users to acquire critical competencies, and promotes technological, pedagogical, and organizational innovation, arguing that social media has a variety of learning content 26 . Users can post digital content online, enabling learners to tap into tacit knowledge while supporting collaboration between learners and teachers. Cao and Hong 27 investigated the antecedents and consequences of social media use in teaching among 249 full-time and part-time faculty members, who reported that the factors for using social media in teaching included personal social media engagement and readiness, external pressures; expected benefits; and perceived risks. The types of Innovators, Early adopters, Early majority, Late majority, Laggards, and objectors. Cao et al. 18 studied the educational effectiveness of 168 teachers' use of social media in university teaching. Their findings suggest that social media use has a positive impact on student learning outcomes and satisfaction. Their research model provides educators with ideas on using social media in the education classroom to improve student performance. Maqableh et al. 28 investigated the use of social networking sites by 366 undergraduate students, and they found that weekly use of social networking sites had a significant impact on student's academic performance and that using social networking sites had a significant impact on improving students' effective time management, and awareness of multitasking. All of the above studies indicate the researcher’s research on social media aids in teaching and learning. All of these studies indicate the positive impact of social media on teaching and learning.

Learning self-efficacy

For the definition of concepts related to learning self-efficacy, scholars have mainly drawn on the idea proposed by Bandura 29 that defines self-efficacy as “the degree to which people feel confident in their ability to use the skills they possess to perform a task”. Self-efficacy is an assessment of a learner’s confidence in his or her ability to use the skills he or she possesses to complete a learning task and is a subjective judgment and feeling about the individual’s ability to control his or her learning behavior and performance 30 . Liu 31 has defined self-efficacy as the belief’s individuals hold about their motivation to act, cognitive ability, and ability to perform to achieve their goals, showing the individual's evaluation and judgment of their abilities. Zhang (2015) showed that learning efficacy is regarded as the degree of belief and confidence that expresses the success of learning. Yan 32 showed the extent to which learning self-efficacy is viewed as an individual. Pan 33 suggested that learning self-efficacy in an online learning environment is a belief that reflects the learner's ability to succeed in the online learning process. Kang 34 believed that learning self-efficacy is the learner's confidence and belief in his or her ability to complete a learning task. Huang 35 considered self-efficacy as an individual’s self-assessment of his or her ability to complete a particular task or perform a specific behavior and the degree of confidence in one’s ability to achieve a specific goal. Kong 36 defined learning self-efficacy as an individual’s judgment of one’s ability to complete academic tasks.

Based on the above analysis, we found that scholars' focus on learning self-efficacy is on learning behavioral efficacy and learning ability efficacy, so this study divides learning self-efficacy into learning behavioral efficacy and learning ability efficacy for further analysis and research 37 , 38 . Search the CNKI database and ProQuest Dissertations for keywords such as “design students’ learning self-efficacy”, “design classroom self-efficacy”, “design learning self-efficacy”, and other keywords. There are few relevant pieces of literature about design majors. Qiu 39 showed that mobile learning-assisted classroom teaching can control the source of self-efficacy from many aspects, thereby improving students’ sense of learning efficacy and helping middle and lower-level students improve their sense of learning efficacy from all dimensions. Yin and Xu 40 argued that the three elements of the network environment—“learning content”, “learning support”, and “social structure of learning”—all have an impact on university students’ learning self-efficacy. Duo et al. 41 recommend that learning activities based on the mobile network learning community increase the trust between students and the sense of belonging in the learning community, promote mutual communication and collaboration between students, and encourage each other to stimulate their learning motivation. In the context of social media applications, self-efficacy refers to the level of confidence that teachers can successfully use social media applications in the classroom 18 . Researchers have found that self-efficacy is related to social media applications 42 . Students had positive experiences with social media applications through content enhancement, creativity experiences, connectivity enrichment, and collaborative engagement 26 . Students who wish to communicate with their tutors in real-time find social media tools such as web pages, blogs, and virtual interactions very satisfying 27 . Overall, students report their enjoyment of different learning processes through social media applications; simultaneously, they show satisfactory tangible achievement of tangible learning outcomes 18 . According to Bandura's 'triadic interaction theory’, Bian 43 and Shi 44 divided learning self-efficacy into two main elements, basic competence, and control, where basic competence includes the individual's sense of effort, competence, the individual sense of the environment, and the individual's sense of control over behavior. The primary sense of competence includes the individual's Sense of effort, competence, environment, and control over behavior. In this study, learning self-efficacy is divided into Learning behavioral efficacy and Learning ability efficacy. Learning behavioral efficacy includes individuals' sense of effort, environment, and control; learning ability efficacy includes individuals' sense of ability, belief, and interest.

In Fig. 1 , learning self-efficacy includes learning behavior efficacy and learning ability efficacy, in which the learning behavior efficacy is determined by the sense of effort, the sense of environment, the sense of control, and the learning ability efficacy is determined by the sense of ability, sense of belief, sense of interest. “Sense of effort” is the understanding of whether one can study hard. Self-efficacy includes the estimation of self-effort and the ability, adaptability, and creativity shown in a particular situation. One with a strong sense of learning self-efficacy thinks they can study hard and focus on tasks 44 . “Sense of environment” refers to the individual’s feeling of their learning environment and grasp of the environment. The individual is the creator of the environment. A person’s feeling and grasp of the environment reflect the strength of his sense of efficacy to some extent. A person with a shared sense of learning self-efficacy is often dissatisfied with his environment, but he cannot do anything about it. He thinks the environment can only dominate him. A person with a high sense of learning self-efficacy will be more satisfied with his school and think that his teachers like him and are willing to study in school 44 . “Sense of control” is an individual’s sense of control over learning activities and learning behavior. It includes the arrangement of individual learning time, whether they can control themselves from external interference, and so on. A person with a strong sense of self-efficacy will feel that he is the master of action and can control the behavior and results of learning. Such a person actively participates in various learning activities. When he encounters difficulties in learning, he thinks he can find a way to solve them, is not easy to be disturbed by the outside world, and can arrange his own learning time. The opposite is the sense of losing control of learning behavior 44 . “Sense of ability” includes an individual’s perception of their natural abilities, expectations of learning outcomes, and perception of achieving their learning goals. A person with a high sense of learning self-efficacy will believe that he or she is brighter and more capable in all areas of learning; that he or she is more confident in learning in all subjects. In contrast, people with low learning self-efficacy have a sense of powerlessness. They are self-doubters who often feel overwhelmed by their learning and are less confident that they can achieve the appropriate learning goals 44 . “Sense of belief” is when an individual knows why he or she is doing something, knows where he or she is going to learn, and does not think before he or she even does it: What if I fail? These are meaningless, useless questions. A person with a high sense of learning self-efficacy is more robust, less afraid of difficulties, and more likely to reach their learning goals. A person with a shared sense of learning self-efficacy, on the other hand, is always going with the flow and is uncertain about the outcome of their learning, causing them to fall behind. “Sense of interest” is a person's tendency to recognize and study the psychological characteristics of acquiring specific knowledge. It is an internal force that can promote people's knowledge and learning. It refers to a person's positive cognitive tendency and emotional state of learning. A person with a high sense of self-efficacy in learning will continue to concentrate on studying and studying, thereby improving learning. However, one with low learning self-efficacy will have psychology such as not being proactive about learning, lacking passion for learning, and being impatient with learning. The elements of learning self-efficacy can be quantified and detailed in the following Fig. 1 .

Learning self-efficacy research structure in this paper.

Research participants

All the procedures were conducted in adherence to the guidelines and regulations set by the institution. Prior to initiating the study, informed consent was obtained in writing from the participants, and the Institutional Review Board for Behavioral and Human Movement Sciences at Nanning Normal University granted approval for all protocols.

Two parallel classes are pre-selected as experimental subjects in our study, one as the experimental group and one as the control group. Social media assisted classroom teaching to intervene in the experimental group, while the control group did not intervene. When selecting the sample, it is essential to consider, as far as possible, the shortcomings of not using randomization to select or assign the study participants, resulting in unequal experimental and control groups. When selecting the experimental subjects, classes with no significant differences in initial status and external conditions, i.e. groups with homogeneity, should be selected. Our study finally decided to select a total of 44 students from Class 2021 Design 1 and a total of 29 students from Class 2021 Design 2, a total of 74 students from Nanning Normal University, as the experimental subjects. The former served as the experimental group, and the latter served as the control group. 73 questionnaires are distributed to measure before the experiment, and 68 are returned, with a return rate of 93.15%. According to the statistics, there were 8 male students and 34 female students in the experimental group, making a total of 44 students (mirrors the demographic trends within the humanities and arts disciplines from which our sample was drawn); there are 10 male students and 16 female students in the control group, making a total of 26 students, making a total of 68 students in both groups. The sample of those who took the course were mainly sophomores, with a small number of first-year students and juniors, which may be related to the nature of the subject of this course and the course system offered by the university. From the analysis of students' majors, liberal arts students in the experimental group accounted for the majority, science students and art students accounted for a small part. In contrast, the control group had more art students, and liberal arts students and science students were small. In the daily self-study time, the experimental and control groups are 2–3 h. The demographic information of research participants is shown in Table 1 .

Research procedure

Firstly, the ADDIE model is used for the innovative design of the teaching method of the course. The number of students in the experimental group was 44, 8 male and 35 females; the number of students in the control group was 29, 10 male and 19 females. Secondly, the classes are targeted at students and applied. Thirdly, the course for both the experimental and control classes is a convenient and practice-oriented course, with the course title “Graphic Design and Production”, which focuses on learning the graphic design software Photoshop. The course uses different cases to explain in detail the process and techniques used to produce these cases using Photoshop, and incorporates practical experience as well as relevant knowledge in the process, striving to achieve precise and accurate operational steps; at the end of the class, the teacher assigns online assignments to be completed on social media, allowing students to post their edited software tutorials online so that students can master the software functions. The teacher assigns online assignments to be completed on social media at the end of the lesson, allowing students to post their editing software tutorials online so that they can master the software functions and production skills, inspire design inspiration, develop design ideas and improve their design skills, and improve students' learning self-efficacy through group collaboration and online interaction. Fourthly, pre-tests and post-tests are conducted in the experimental and control classes before the experiment. Fifthly, experimental data are collected, analyzed, and summarized.

We use a questionnaire survey to collect data. Self-efficacy is a person’s subjective judgment on whether one can successfully perform a particular achievement. American psychologist Albert Bandura first proposed it. To understand the improvement effect of students’ self-efficacy after the experimental intervention, this work questionnaire was referenced by the author from “Self-efficacy” “General Perceived Self Efficacy Scale” (General Perceived Self Efficacy Scale) German psychologist Schwarzer and Jerusalem (1995) and “Academic Self-Efficacy Questionnaire”, a well-known Chinese scholar Liang 45 . The questionnaire content is detailed in the supplementary information . A pre-survey of the questionnaire is conducted here. The second-year students of design majors collected 32 questionnaires, eliminated similar questions based on the data, and compiled them into a formal survey scale. The scale consists of 54 items, 4 questions about basic personal information, and 50 questions about learning self-efficacy. The Likert five-point scale is the questionnaire used in this study. The answers are divided into “completely inconsistent", “relatively inconsistent”, “unsure”, and “relatively consistent”. The five options of “Completely Meet” and “Compliant” will count as 1, 2, 3, 4, and 5 points, respectively. Divided into a sense of ability (Q5–Q14), a sense of effort (Q15–Q20), a sense of environment (Q21–Q28), a sense of control (Q29–Q36), a sense of Interest (Q37–Q45), a sense of belief (Q46–Q54). To demonstrate the scientific effectiveness of the experiment, and to further control the influence of confounding factors on the experimental intervention. This article thus sets up a control group as a reference. Through the pre-test and post-test in different periods, comparison of experimental data through pre-and post-tests to illustrate the effects of the intervention.

Reliability indicates the consistency of the results of a measurement scale (See Table 2 ). It consists of intrinsic and extrinsic reliability, of which intrinsic reliability is essential. Using an internal consistency reliability test scale, a Cronbach's alpha coefficient of reliability statistics greater than or equal to 0.9 indicates that the scale has good reliability, 0.8–0.9 indicates good reliability, 7–0.8 items are acceptable. Less than 0.7 means to discard some items in the scale 46 . This study conducted a reliability analysis on the effects of the related 6-dimensional pre-test survey to illustrate the reliability of the questionnaire.

From the Table 2 , the Cronbach alpha coefficients for the pre-test, sense of effort, sense of environment, sense of control, sense of interest, sense of belief, and the total questionnaire, were 0.919, 0.839, 0.848, 0.865, 0.852, 0.889 and 0.958 respectively. The post-test Cronbach alpha coefficients were 0.898, 0.888, 0.886, 0.889, 0.900, 0.893 and 0.970 respectively. The Cronbach alpha coefficients were all greater than 0.8, indicating a high degree of reliability of the measurement data.

The validity, also known as accuracy, reflects how close the measurement result is to the “true value”. Validity includes structure validity, content validity, convergent validity, and discriminative validity. Because the experiment is a small sample study, we cannot do any specific factorization. KMO and Bartlett sphericity test values are an important part of structural validity. Indicator, general validity evaluation (KMO value above 0.9, indicating very good validity; 0.8–0.9, indicating good validity; 0.7–0.8 validity is good; 0.6–0.7 validity is acceptable; 0.5–0.6 means poor validity; below 0.45 means that some items should be abandoned.

Table 3 shows that the KMO values of ability, effort, environment, control, interest, belief, and the total questionnaire are 0.911, 0.812, 0.778, 0.825, 0.779, 0.850, 0.613, and the KMO values of the post-test are respectively. The KMO values are 0.887, 0.775, 0.892, 0.868, 0.862, 0.883, 0.715. KMO values are basically above 0.8, and all are greater than 0.6. This result indicates that the validity is acceptable, the scale has a high degree of reasonableness, and the valid data.

In the graphic design and production (professional design course), we will learn the practical software with cases. After class, we will share knowledge on the self-media platform. We will give face-to-face computer instruction offline from 8:00 to 11:20 every Wednesday morning for 16 weeks. China's top online sharing platform (APP) is Tik Tok, micro-blog (Micro Blog) and Xiao hong shu. The experiment began on September 1, 2022, and conducted the pre-questionnaire survey simultaneously. At the end of the course, on January 6, 2023, the post questionnaire survey was conducted. A total of 74 questionnaires were distributed in this study, recovered 74 questionnaires. After excluding the invalid questionnaires with incomplete filling and wrong answers, 68 valid questionnaires were obtained, with an effective rate of 91%, meeting the test requirements. Then, use the social science analysis software SPSS Statistics 26 to analyze the data: (1) descriptive statistical analysis of the dimensions of learning self-efficacy; (2) Using correlation test to analyze the correlation between learning self-efficacy and the use of social media; (3) This study used a comparative analysis of group differences to detect the influence of learning self-efficacy on various dimensions of social media and design courses. For data processing and analysis, use the spss26 version software and frequency statistics to create statistics on the basic situation of the research object and the basic situation of the use of live broadcast. The reliability scale analysis (internal consistency test) and use Bartlett's sphericity test to illustrate the reliability and validity of the questionnaire and the individual differences between the control group and the experimental group in demographic variables (gender, grade, Major, self-study time per day) are explained by cross-analysis (chi-square test). In the experimental group and the control group, the pre-test, post-test, before-and-after test of the experimental group and the control group adopt independent sample T-test and paired sample T-test to illustrate the effect of the experimental intervention (The significance level of the test is 0.05 two-sided).

Results and discussion

Comparison of pre-test and post-test between groups.

To study whether the data of the experimental group and the control group are significantly different in the pre-test and post-test mean of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. The research for this situation uses an independent sample T-test and an independent sample. The test needs to meet some false parameters, such as normality requirements. Generally passing the normality test index requirements are relatively strict, so it can be relaxed to obey an approximately normal distribution. If there is serious skewness distribution, replace it with the nonparametric test. Variables are required to be continuous variables. The six variables in this study define continuous variables. The variable value information is independent of each other. Therefore, we use the independent sample T-test.

From the Table 4 , a pre-test found that there was no statistically significant difference between the experimental group and the control group at the 0.05 confidence level ( p > 0.05) for perceptions of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. Before the experiment, the two groups of test groups have the same quality in measuring self-efficacy. The experimental class and the control class are homogeneous groups. Table 5 shows the independent samples t-test for the post-test, used to compare the experimental and control groups on six items, including the sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief.

The experimental and control groups have statistically significant scores ( p < 0.05) for sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief, and the experimental and control groups have statistically significant scores (t = 3.177, p = 0.002) for a sense of competence. (t = 3.177, p = 0.002) at the 0.01 level, with the experimental group scoring significantly higher (3.91 ± 0.51) than the control group (3.43 ± 0.73). The experimental group and the control group showed significance for the perception of effort at the 0.01 confidence level (t = 2.911, p = 0.005), with the experimental group scoring significantly higher (3.88 ± 0.66) than the control group scoring significantly higher (3.31 ± 0.94). The experimental and control groups show significance at the 0.05 level (t = 2.451, p = 0.017) for the sense of environment, with the experimental group scoring significantly higher (3.95 ± 0.61) than the control group scoring significantly higher (3.58 ± 0.62). The experimental and control groups showed significance for sense of control at the 0.05 level of significance (t = 2.524, p = 0.014), and the score for the experimental group (3.76 ± 0.67) would be significantly higher than the score for the control group (3.31 ± 0.78). The experimental and control groups showed significance at the 0.01 level for sense of interest (t = 2.842, p = 0.006), and the experimental group's score (3.87 ± 0.61) would be significantly higher than the control group's score (3.39 ± 0.77). The experimental and control groups showed significance at the 0.01 level for the sense of belief (t = 3.377, p = 0.001), and the experimental group would have scored significantly higher (4.04 ± 0.52) than the control group (3.56 ± 0.65). Therefore, we can conclude that the experimental group's post-test significantly affects the mean scores of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. A social media-assisted course has a positive impact on students' self-efficacy.

Comparison of pre-test and post-test of each group

The paired-sample T-test is an extension of the single-sample T-test. The purpose is to explore whether the means of related (paired) groups are significantly different. There are four standard paired designs: (1) Before and after treatment of the same subject Data, (2) Data from two different parts of the same subject, (3) Test results of the same sample with two methods or instruments, 4. Two matched subjects receive two treatments, respectively. This study belongs to the first type, the 6 learning self-efficacy dimensions of the experimental group and the control group is measured before and after different periods.

Paired t-tests is used to analyze whether there is a significant improvement in the learning self-efficacy dimension in the experimental group after the experimental social media-assisted course intervention. In Table 6 , we can see that the six paired data groups showed significant differences ( p < 0.05) in the pre and post-tests of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. There is a level of significance of 0.01 (t = − 4.540, p = 0.000 < 0.05) before and after the sense of ability, the score after the sense of ability (3.91 ± 0.51), and the score before the Sense of ability (3.41 ± 0.55). The level of significance between the pre-test and post-test of sense of effort is 0.01 (t = − 4.002, p = 0.000). The score of the sense of effort post-test (3.88 ± 0.66) will be significantly higher than the average score of the sense of effort pre-test (3.31 ± 0.659). The significance level between the pre-test and post-test Sense of environment is 0.01 (t = − 3.897, p = 0.000). The average score for post- Sense of environment (3.95 ± 0.61) will be significantly higher than that of sense of environment—the average score of the previous test (3.47 ± 0.44). The average value of a post- sense of control (3.76 ± 0.67) will be significantly higher than the average of the front side of the Sense of control value (3.27 ± 0.52). The sense of interest pre-test and post-test showed a significance level of 0.01 (− 4.765, p = 0.000), and the average value of Sense of interest post-test was 3.87 ± 0.61. It would be significantly higher than the average value of the Sense of interest (3.25 ± 0.59), the significance between the pre-test and post-test of belief sensing is 0.01 level (t = − 3.939, p = 0.000). Thus, the average value of a post-sense of belief (4.04 ± 0.52) will be significantly higher than that of a pre-sense of belief Average value (3.58 ± 0.58). After the experimental group’s post-test, the scores for the Sense of ability, effort, environment, control, interest, and belief before the comparison experiment increased significantly. This result has a significant improvement effect. Table 7 shows that the control group did not show any differences in the pre and post-tests using paired t-tests on the dimensions of learning self-efficacy such as sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief ( p > 0.05). It shows no experimental intervention for the control group, and it does not produce a significant effect.

The purpose of this study aims to explore the impact of social media use on college students' learning self-efficacy, examine the changes in the elements of college students' learning self-efficacy before and after the experiment, and make an empirical study to enrich the theory. This study developed an innovative design for course teaching methods using the ADDIE model. The design process followed a series of model rules of analysis, design, development, implementation, and evaluation, as well as conducted a descriptive statistical analysis of the learning self-efficacy of design undergraduates. Using questionnaires and data analysis, the correlation between the various dimensions of learning self-efficacy is tested. We also examined the correlation between the two factors, and verifies whether there was a causal relationship between the two factors.

Based on prior research and the results of existing practice, a learning self-efficacy is developed for university students and tested its reliability and validity. The scale is used to pre-test the self-efficacy levels of the two subjects before the experiment, and a post-test of the self-efficacy of the two groups is conducted. By measuring and investigating the learning self-efficacy of the study participants before the experiment, this study determined that there was no significant difference between the experimental group and the control group in terms of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. Before the experiment, the two test groups had homogeneity in measuring the dimensionality of learning self-efficacy. During the experiment, this study intervened in social media assignments for the experimental group. The experiment used learning methods such as network assignments, mutual aid communication, mutual evaluation of assignments, and group discussions. After the experiment, the data analysis showed an increase in learning self-efficacy in the experimental group compared to the pre-test. With the test time increased, the learning self-efficacy level of the control group decreased slightly. It shows that social media can promote learning self-efficacy to a certain extent. This conclusion is similar to Cao et al. 18 , who suggested that social media would improve educational outcomes.

We have examined the differences between the experimental and control group post-tests on six items, including the sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. This result proves that a social media-assisted course has a positive impact on students' learning self-efficacy. Compared with the control group, students in the experimental group had a higher interest in their major. They showed that they liked to share their learning experiences and solve difficulties in their studies after class. They had higher motivation and self-directed learning ability after class than students in the control group. In terms of a sense of environment, students in the experimental group were more willing to share their learning with others, speak boldly, and participate in the environment than students in the control group.

The experimental results of this study showed that the experimental group showed significant improvement in the learning self-efficacy dimensions after the experimental intervention in the social media-assisted classroom, with significant increases in the sense of ability, sense of effort, sense of environment, sense of control, sense of interest and sense of belief compared to the pre-experimental scores. This result had a significant improvement effect. Evidence that a social media-assisted course has a positive impact on students' learning self-efficacy. Most of the students recognized the impact of social media on their learning self-efficacy, such as encouragement from peers, help from teachers, attention from online friends, and recognition of their achievements, so that they can gain a sense of achievement that they do not have in the classroom, which stimulates their positive perception of learning and is more conducive to the awakening of positive effects. This phenomenon is in line with Ajjan and Hartshorne 2 . They argue that social media provides many opportunities for learners to publish their work globally, which brings many benefits to teaching and learning. The publication of students' works online led to similar positive attitudes towards learning and improved grades and motivation. This study also found that students in the experimental group in the post-test controlled their behavior, became more interested in learning, became more purposeful, had more faith in their learning abilities, and believed that their efforts would be rewarded. This result is also in line with Ajjan and Hartshorne's (2008) indication that integrating Web 2.0 technologies into classroom learning environments can effectively increase students' satisfaction with the course and improve their learning and writing skills.

We only selected students from one university to conduct a survey, and the survey subjects were self-selected. Therefore, the external validity and generalizability of our study may be limited. Despite the limitations, we believe this study has important implications for researchers and educators. The use of social media is the focus of many studies that aim to assess the impact and potential of social media in learning and teaching environments. We hope that this study will help lay the groundwork for future research on the outcomes of social media utilization. In addition, future research should further examine university support in encouraging teachers to begin using social media and university classrooms in supporting social media (supplementary file 1 ).

The present study has provided preliminary evidence on the positive association between social media integration in education and increased learning self-efficacy among college students. However, several avenues for future research can be identified to extend our understanding of this relationship.

Firstly, replication studies with larger and more diverse samples are needed to validate our findings across different educational contexts and cultural backgrounds. This would enhance the generalizability of our results and provide a more robust foundation for the use of social media in teaching. Secondly, longitudinal investigations should be conducted to explore the sustained effects of social media use on learning self-efficacy. Such studies would offer insights into how the observed benefits evolve over time and whether they lead to improved academic performance or other relevant outcomes. Furthermore, future research should consider the exploration of potential moderators such as individual differences in students' learning styles, prior social media experience, and psychological factors that may influence the effectiveness of social media in education. Additionally, as social media platforms continue to evolve rapidly, it is crucial to assess the impact of emerging features and trends on learning self-efficacy. This includes an examination of advanced tools like virtual reality, augmented reality, and artificial intelligence that are increasingly being integrated into social media environments. Lastly, there is a need for research exploring the development and evaluation of instructional models that effectively combine traditional teaching methods with innovative uses of social media. This could guide educators in designing courses that maximize the benefits of social media while minimizing potential drawbacks.

In conclusion, the current study marks an important step in recognizing the potential of social media as an educational tool. Through continued research, we can further unpack the mechanisms by which social media can enhance learning self-efficacy and inform the development of effective educational strategies in the digital age.

Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request. The data are not publicly available due to privacy or ethical restrictions.

Rasheed, M. I. et al. Usage of social media, student engagement, and creativity: The role of knowledge sharing behavior and cyberbullying. Comput. Educ. 159 , 104002 (2020).

Article Google Scholar

Ajjan, H. & Hartshorne, R. Investigating faculty decisions to adopt Web 2.0 technologies: Theory and empirical tests. Internet High. Educ. 11 , 71–80 (2008).

Maloney, E. J. What web 2.0 can teach us about learning. The Chronicle of Higher Education 53 , B26–B27 (2007).

Ustun, A. B., Karaoglan-Yilmaz, F. G. & Yilmaz, R. Educational UTAUT-based virtual reality acceptance scale: A validity and reliability study. Virtual Real. 27 , 1063–1076 (2023).

Schunk, D. H. Self-efficacy and classroom learning. Psychol. Sch. 22 , 208–223 (1985).

Cheung, W., Li, E. Y. & Yee, L. W. Multimedia learning system and its effect on self-efficacy in database modeling and design: An exploratory study. Comput. Educ. 41 , 249–270 (2003).

Bates, R. & Khasawneh, S. Self-efficacy and college students’ perceptions and use of online learning systems. Comput. Hum. Behav. 23 , 175–191 (2007).

Shen, D., Cho, M.-H., Tsai, C.-L. & Marra, R. Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. Internet High. Educ. 19 , 10–17 (2013).

Chiu, S.-I. The relationship between life stress and smartphone addiction on taiwanese university student: A mediation model of learning self-efficacy and social self-Efficacy. Comput. Hum. Behav. 34 , 49–57 (2014).

Kim, S.-O. & Kang, B.-H. The influence of nursing students’ learning experience, recognition of importance and learning self-efficacy for core fundamental nursing skills on their self-confidence. J. Korea Acad.-Ind. Coop. Soc. 17 , 172–182 (2016).

Google Scholar

Paciello, M., Ghezzi, V., Tramontano, C., Barbaranelli, C. & Fida, R. Self-efficacy configurations and wellbeing in the academic context: A person-centred approach. Pers. Individ. Differ. 99 , 16–21 (2016).

Suprapto, N., Chang, T.-S. & Ku, C.-H. Conception of learning physics and self-efficacy among Indonesian University students. J. Balt. Sci. Educ. 16 , 7–19 (2017).

Kumar, J. A., Bervell, B., Annamalai, N. & Osman, S. Behavioral intention to use mobile learning: Evaluating the role of self-efficacy, subjective norm, and WhatsApp use habit. IEEE Access 8 , 208058–208074 (2020).

Fisk, J. E. & Warr, P. Age-related impairment in associative learning: The role of anxiety, arousal and learning self-efficacy. Pers. Indiv. Differ. 21 , 675–686 (1996).

Pence, H. E. Preparing for the real web generation. J. Educ. Technol. Syst. 35 , 347–356 (2007).

Hu, J., Lee, J. & Yi, X. Blended knowledge sharing model in design professional. Sci. Rep. 13 , 16326 (2023).

Article ADS CAS PubMed PubMed Central Google Scholar

Moran, M., Seaman, J. & Tintikane, H. Blogs, wikis, podcasts and Facebook: How today’s higher education faculty use social media, vol. 22, 1–28 (Pearson Learning Solutions. Retrieved December, 2012).

Cao, Y., Ajjan, H. & Hong, P. Using social media applications for educational outcomes in college teaching: A structural equation analysis: Social media use in teaching. Br. J. Educ. Technol. 44 , 581–593 (2013).

Artino, A. R. Academic self-efficacy: From educational theory to instructional practice. Perspect. Med. Educ. 1 , 76–85 (2012).

Article PubMed PubMed Central Google Scholar

Pajares, F. Self-efficacy beliefs in academic settings. Rev. Educ. Res. 66 , 543–578 (1996).

Zhao, Z. Classroom Teaching Design of Layout Design Based on Self Efficacy Theory (Tianjin University of Technology and Education, 2021).

Yılmaz, F. G. K. & Yılmaz, R. Exploring the role of sociability, sense of community and course satisfaction on students’ engagement in flipped classroom supported by facebook groups. J. Comput. Educ. 10 , 135–162 (2023).

Nguyen, N. P., Yan, G. & Thai, M. T. Analysis of misinformation containment in online social networks. Comput. Netw. 57 , 2133–2146 (2013).

Connaway, L. S., Radford, M. L., Dickey, T. J., Williams, J. D. A. & Confer, P. Sense-making and synchronicity: Information-seeking behaviors of millennials and baby boomers. Libri 58 , 123–135 (2008).

Wankel, C., Marovich, M. & Stanaityte, J. Cutting-edge social media approaches to business education : teaching with LinkedIn, Facebook, Twitter, Second Life, and blogs . (Global Management Journal, 2010).

Redecker, C., Ala-Mutka, K. & Punie, Y. Learning 2.0: The impact of social media on learning in Europe. Policy brief. JRC Scientific and Technical Report. EUR JRC56958 EN . Available from http://bit.ly/cljlpq [Accessed 6 th February 2011] 6 (2010).

Cao, Y. & Hong, P. Antecedents and consequences of social media utilization in college teaching: A proposed model with mixed-methods investigation. Horizon 19 , 297–306 (2011).

Maqableh, M. et al. The impact of social media networks websites usage on students’ academic performance. Commun. Netw. 7 , 159–171 (2015).

Bandura, A. Self-Efficacy (Worth Publishers, 1997).

Karaoglan-Yilmaz, F. G., Ustun, A. B., Zhang, K. & Yilmaz, R. Metacognitive awareness, reflective thinking, problem solving, and community of inquiry as predictors of academic self-efficacy in blended learning: A correlational study. Turk. Online J. Distance Educ. 24 , 20–36 (2023).

Liu, W. Self-efficacy Level and Analysis of Influencing Factors on Non-English Major Bilingual University Students—An Investigation Based on Three (Xinjiang Normal University, 2015).

Yan, W. Influence of College Students’ Positive Emotions on Learning Engagement and Academic Self-efficacy (Shanghai Normal University, 2016).

Pan, J. Relational Model Construction between College Students’ Learning Self-efficacy and Their Online Autonomous Learning Ability (Northeast Normal University, 2017).

Kang, Y. The Study on the Relationship Between Learning Motivation, Self-efficacy and Burnout in College Students (Shanxi University of Finance and Economics, 2018).

Huang, L. A Study on the Relationship between Chinese Learning Efficacy and Learning Motivation of Foreign Students in China (Huaqiao University, 2018).

Kong, W. Research on the Mediating Role of Undergraduates’ Learning Self-efficacy in the Relationship between Professional Identification and Learning Burnout (Shanghai Normal University, 2019).

Kuo, T. M., Tsai, C. C. & Wang, J. C. Linking web-based learning self-efficacy and learning engagement in MOOCs: The role of online academic hardiness. Internet High. Educ. 51 , 100819 (2021).

Zhan, Y. A Study of the Impact of Social Media Use and Dependence on Real-Life Social Interaction Among University Students (Shanghai International Studies University, 2020).

Qiu, S. A study on mobile learning to assist in developing English learning effectiveness among university students. J. Lanzhou Inst. Educ. 33 , 138–140 (2017).

Yin, R. & Xu, D. A study on the relationship between online learning environment and university students’ learning self-efficacy. E-educ. Res. 9 , 46–52 (2011).

Duo, Z., Zhao, W. & Ren, Y. A New paradigm for building mobile online learning communities: A perspective on the development of self-regulated learning efficacy among university students, in Modern distance education 10–17 (2019).

Park, S. Y., Nam, M.-W. & Cha, S.-B. University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model: Factors related to use mobile learning. Br. J. Educ. Technol. 43 , 592–605 (2012).

Bian, Y. Development and application of the Learning Self-Efficacy Scale (East China Normal University, 2003).

Shi, X. Between Life Stress and Smartphone Addiction on Taiwanese University Student (Southwest University, 2010).

Liang, Y. Study On Achievement Goals、Attribution Styles and Academic Self-efficacy of Collage Students (Central China Normal University, 2000).

Qiu, H. Quantitative Research and Statistical Analysis (Chongqing University Press, 2013).

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Acknowledgements

This work is supported by the 2023 Guangxi University Young and middle-aged Teachers' Basic Research Ability Enhancement Project—“Research on Innovative Communication Strategies and Effects of Zhuang Traditional Crafts from the Perspective of the Metaverse” (Grant Nos. 2023KY0385), and the special project on innovation and entrepreneurship education in universities under the “14th Five-Year Plan” for Guangxi Education Science in 2023, titled “One Core, Two Directions, Three Integrations - Strategy and Practical Research on Innovation and Entrepreneurship Education in Local Universities” (Grant Nos. 2023ZJY1955), and the 2023 Guangxi Higher Education Undergraduate Teaching Reform General Project (Category B) “Research on the Construction and Development of PBL Teaching Model in Advertising” (Grant Nos.2023JGB294), and the 2022 Guangxi Higher Education Undergraduate Teaching Reform Project (General Category A) “Exploration and Practical Research on Public Art Design Courses in Colleges and Universities under Great Aesthetic Education” (Grant Nos. 2022JGA251), and the 2023 Guangxi Higher Education Undergraduate Teaching Reform Project Key Project “Research and Practice on the Training of Interdisciplinary Composite Talents in Design Majors Based on the Concept of Specialization and Integration—Taking Guangxi Institute of Traditional Crafts as an Example” (Grant Nos. 2023JGZ147), and the2024 Nanning Normal University Undergraduate Teaching Reform Project “Research and Practice on the Application of “Guangxi Intangible Cultural Heritage” in Packaging Design Courses from the Ideological and Political Perspective of the Curriculum” (Grant Nos. 2024JGX048),and the 2023 Hubei Normal University Teacher Teaching Reform Research Project (Key Project) -Curriculum Development for Improving Pre-service Music Teachers' Teaching Design Capabilities from the Perspective of OBE (Grant Nos. 2023014), and the 2023 Guangxi Education Science “14th Five-Year Plan” special project: “Specialized Integration” Model and Practice of Art and Design Majors in Colleges and Universities in Ethnic Areas Based on the OBE Concept (Grant Nos. 2023ZJY1805), and the 2024 Guangxi University Young and Middle-aged Teachers’ Scientific Research Basic Ability Improvement Project “Research on the Integration Path of University Entrepreneurship and Intangible Inheritance - Taking Liu Sanjie IP as an Example” (Grant Nos. 2024KY0374), and the 2022 Research Project on the Theory and Practice of Ideological and Political Education for College Students in Guangxi - “Party Building + Red”: Practice and Research on the Innovation of Education Model in College Student Dormitories (Grant Nos. 2022SZ028), and the 2021 Guangxi University Young and Middle-aged Teachers’ Scientific Research Basic Ability Improvement Project - "Research on the Application of Ethnic Elements in the Visual Design of Live Broadcast Delivery of Guangxi Local Products" (Grant Nos. 2021KY0891).

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The contribution of H. to this paper primarily lies in research design and experimental execution. H. was responsible for the overall framework design of the paper, setting research objectives and methods, and actively participating in data collection and analysis during the experimentation process. Furthermore, H. was also responsible for conducting literature reviews and played a crucial role in the writing and editing phases of the paper. L.'s contribution to this paper primarily manifests in theoretical derivation and the discussion section. Additionally, author L. also proposed future research directions and recommendations in the discussion section, aiming to facilitate further research explorations. Y.'s contribution to this paper is mainly reflected in data analysis and result interpretation. Y. was responsible for statistically analyzing the experimental data and employing relevant analytical tools and techniques to interpret and elucidate the data results.

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Hu, J., Lai, Y. & Yi, X. Effectiveness of social media-assisted course on learning self-efficacy. Sci Rep 14 , 10112 (2024). https://doi.org/10.1038/s41598-024-60724-0

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Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections. Psychology and Health, 26(9), 1113–1127. https://doi.org/10.1080/08870446.2011.613995

Angkasa, P. (2023). PENGARUH PENGGUNAAN SMARTPHONE TERHADAP PRESTASI BELAJAR SISWA SMK ADI SANGGORO BOGOR (Vol. 4, Issue 1).

Ansari, J. A. N., & Khan, N. A. (2020). Exploring the role of social media in collaborative learning the new domain of learning. Smart Learning Environments, 7(1). https://doi.org/10.1186/s40561-020-00118-7

Arif Setiawan, A., Wijayanti, C. N., Yuliatmojo, W., Setiawan, A. A., & Surakarta, U. (2022). Moralitas Bermedia Sosial (Distorsi Etika dalam Perspektif Halal Media : Standar Etika Komunikasi Publik). AGUNA: Jurnal Ilmu Komunikasi, 3(1), 38–46.

Ashraf, M. A., Khan, M. N., Chohan, S. R., Khan, M., Rafique, W., Farid, M. F., & Khan, A. U. (2021). Social media improves students’ academic performance: exploring the role of social media adoption in the open learning environment among international medical students in china. Healthcare (Switzerland), 9(10). https://doi.org/10.3390/healthcare9101272

Auxier, B. (2020, October 5). 64% of Americans say social media have a mostly negative effect on the way things are going in the U.S. today. Pew Research Center.

Bozzola, E., Spina, G., Agostiniani, R., Barni, S., Russo, R., Scarpato, E., Di Mauro, A., Di Stefano, A. V., Caruso, C., Corsello, G., & Staiano, A. (2022). The Use of Social Media in Children and Adolescents: Scoping Review on the Potential Risks. International Journal of Environmental Research and Public Health, 19(16). https://doi.org/10.3390/ijerph19169960

Harahap, M., Firman, F., & Ahmad, R. (2021). Penggunaan Social Media dan Perubahan Sosial Budaya Masyarakat. EDUKATIF : JURNAL ILMU PENDIDIKAN, 3(1), 135–143. https://doi.org/10.31004/edukatif.v3i1.252

Hoque, M. E. (2016). Three Domains of Learning: Cognitive, Affective and Psychomotor. The Journal of EFL Education and Research, 2(2), 2520–5897.

Izadpanah, S., & Charmi, M. (2022). The effect of social networks on academic self-handicapping with the mediating role of self-regulatory learning strategies and academic achievement among EFL students. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2022.987381

Kominfo. (2019). Kominfo Blokir Ribuan Konten Vulgar dari TikTok hingga Smule. https://www.kominfo.go.id/content/detail/16225/kominfo-blokir-ribuan-konten-vulgar-dari-tiktok-hingga-smule/0/sorotan_media

Krejcie, R. V, & Morgan, D. W. (1970). Determining Sample Size for Research Activities. EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT, 30, 607–610.

Latifah, T. F., Supriyanto, A., Suprihatin, B., & Kurniawan, S. J. (2022). Social Media As Support Career Guidance Services. EDUKATIF : JURNAL ILMU PENDIDIKAN, 4(2), 2950–2961. https://doi.org/10.31004/edukatif.v4i2.2473

O’Flaherty, J., & Doyle, E. (2014). Making the case for moral development education. Journal of Further and Higher Education, 38(2), 147–162. https://doi.org/10.1080/0309877X.2012.699519

Ojo, A. A. (2022). Social Media: Negative Effect on Academic Performance of Secondary School Students In Nigeria. British Journal of Education, 10(3), 126–131.

Pardede, S., Sinaga, D., & Manurung, S. (2022). An Impact Evaluation of New Normal Education for Building Student’s Superior Character. Dinamika Pendidikan, 17(2), 164–176. https://doi.org/10.15294/dp.v17i2.40178

Pransisca Putri, N., Yasmi, F., Kardo, R., Program, M., Bimbingan, S., Konseling, D., Pgri, U., Barat, S., Program, D., Utara, P., & Padang, K. (2023). Pengaruh Penggunaan Media Sosial Terhadap Prestasi Belajar Peserta Didik Pada Kelas XI MIPA di SMA Negeri 5 Padang. Journal on Education, 05(04).

Regasa, A., & Lemmi Ettisa, D. (2023). The Impact of TikTok on Students: A Literature Review. Qeios. https://doi.org/10.32388/epfgo6

Saputra, A. (2019). Survei Penggunaan Media Sosial di Kalangan Mahasiswa Kota Padang Menggunakan Teori Uses dan Gratifications. Baca: Jurnal Dokumentasi Dan Informasi, 40(2), 207. https://doi.org/10.14203/j.baca.v40i2.476

Senjaya, P., Ong, F., & Bing Andika, C. (2020). School Environmental Influences, Student Discipline and Learning Motivation toward Increasing Senior High Students Achievement. International Journal of Innovative Science and Research Technology, 5(1), 2456–2165.

Siwi, A., Utami, F., & Baiti, N. (2018). Pengaruh Media Sosial Terhadap Perilaku Cyber Bullying Pada Kalangan Remaja. 18(2), 2018–2027. https://doi.org/10.31294/jc.v18i2

Susanti, I., & Atmini, N. D. (2022). Pengaruh Kedisiplinan Siswa Terhadap Prestasi Belajar Siswa Kelas Iv Sd Menggunakan Metode Angket Dengan Teknik Cluster Sampling. Jurnal Ilmiah Ekonomi Dan Bisnis, 15(2), 330–337.

Yuliati, S. (2015). Pengaruh Pendidikan Moralitas dan Bimbingan Konseling terhadap Prestasi Belajar. Jurnal Penelitian Dan Pendidikan IPS (JPPI), 9(2), 1858–4985.

Zakir, S., Maiyana, E., & Jehwae, P. (2022). Improving Student Academic Performance Through Web Base Learning. Jurnal Educative: Journal of Educational Studies, 7(2), 173. https://doi.org/10.30983/educative.v7i2.6037

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Case Study Research Method in Psychology

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Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

Famous Case Studies

Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

Studying the effectiveness of psychotherapy approaches with an individual patient
Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

Studying psychological development from birth through adolescence
Cases of learning disabilities, autism spectrum disorders, ADHD
Effects of trauma, abuse, deprivation on development

Types of Case Studies

Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

News/Media – News coverage of events related to the case study.
Academic articles – Journal articles, dissertations etc. that discuss the case.
Government reports – Official data and records related to the case context.
Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
Include client demographics like age and gender, information about social relationships, and mental health history.
Describe all physical, emotional, and/or sensory symptoms reported by the client.
Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
Present the results of the intervention,including any quantitative or qualitative data collected.
For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
Offer clinical implications, and suggest future research directions.

5. Additional Items

Thank specific assistants for writing support only. No patient acknowledgments.
References should directly support any key claims or quotes included.
Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
Provides detailed (rich qualitative) information.
Provides insight for further research.
Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

Lacking scientific rigor and providing little basis for generalization of results to the wider population.
Researchers’ own subjective feelings may influence the case study (researcher bias).
Difficult to replicate.
Time-consuming and expensive.
The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895). Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head. Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868). Recovery from the Passage of an Iron Bar through the Head . Publications of the Massachusetts Medical Society. 2 (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972). Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

Case Study Approach
Case Study Method
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Research Methodology

Qualitative Data Coding

What Is a Focus Group?

Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

Research Methodology , Statistics

What Is Face Validity In Research? Importance & How To Measure

Criterion Validity: Definition & Examples

Survey of machine learning techniques for Arabic fake news detection

Open access
Published: 28 May 2024
Volume 57 , article number 157 , ( 2024 )

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Ibtissam Touahri 1 &
Azzeddine Mazroui 2

Social media platforms have emerged as primary information sources, offering easy access to a wide audience. Consequently, a significant portion of the global population relies on these platforms for updates on current events. However, fraudulent actors exploit social networks to disseminate false information, either for financial gain or to manipulate public opinion. Recognizing the detrimental impact of fake news, researchers have turned their attention to automating its detection. In this paper, we provide a thorough review of fake news detection in Arabic, a low-resource language, to contextualize the current state of research in this domain. In our research methodology, we recall fake news terminology, provide examples for clarity, particularly in Arabic contexts, and explore its impact on public opinion. We discuss the challenges in fake news detection, outline the used datasets, and provide Arabic annotation samples for label assignment. Likewise, preprocessing steps for Arabic language nuances are highlighted. We also explore features from shared tasks and their implications. Lastly, we address open issues, proposing some future research directions like dataset improvement, feature refinement, and increased awareness to combat fake news proliferation. We contend that incorporating our perspective into the examination of fake news aspects, along with suggesting enhancements, sets this survey apart from others currently available.

Avoid common mistakes on your manuscript.

1 Introduction

The advent of Web 2.0 has facilitated real-time human interaction and the rapid dissemination of news. Alongside traditional news outlets, individuals increasingly rely on online platforms to express themselves and gather information. Social networks serve as hubs for a plethora of data, including opinions, news, rumors, and fake news generated by internet users. While these platforms offer instant access to information, they also facilitate the dissemination of unchecked data, which can inadvertently mislead users. The proliferation of fake news on social media, fueled by sensational and inflammatory language that aims at maximizing engagement, is a growing concern. Additionally, social media platforms often employ fear-based and persuasive language in their content, further amplifying the impact of misinformation. Satirical content, in particular, poses a unique challenge as it can skew public perception and be exploited for political and commercial gain.

The dissemination of misleading or fake statements, often appealing to emotions, can significantly influence public opinion and lead individuals to disregard factual information. The 2016 US presidential campaign, widely reported to have been influenced by fake news, brought heightened awareness to the detrimental impact of misinformation (Bovet and Makse 2019 ). Furthermore, during the coronavirus pandemic, claims regarding COVID-19 often circulated without credible references (Shahi et al. 2021 ). Indeed, many studies have reported that on social networks, the pandemic was accompanied by a large amount of fake and misleading news about the virus that has spread faster than the facts (Yafooz et al. 2022 ) (Alhindi et al. 2021 ). For example, fake news had claimed that COVID-19 is caused by 5G technology, which led to a misunderstanding of the pandemic among the public (Touahri and Mazroui 2020 ). Hence, fake news has attracted attention in all countries and cultures, from the US elections to the Arab Spring (Rampersad and Althiyabi 2020 ). Extensive research related to these claims for the English language has been conducted (Zhou et al. 2020 ), but few researches have focused on the Arabic language which has specific characteristics (Shahi et al. 2021 ; Saeed et al. 2018 , 2021 ).

The study of fake news is a multidisciplinary endeavor, bringing together experts from computer and information sciences, as well as political, economic, journalistic, and psychological fields. This collaborative approach is essential for comprehensive understanding and effective solutions.

Online Fake news encompasses various aspects, including the individuals or entities creating the news, the content itself, those disseminating it, the intended targets, and the broader social context in which it circulates (Zhou and Zafarani 2020 ; Wang et al. 2023 ). The primary sources of information vary in terms of trustworthiness, with government communication platforms generally being the most trusted, followed by local news channels, while social media platforms are typically viewed with lower levels of trust (Lim and Perrault 2020 ). People's political orientations can influence their perception of the accuracy of both genuine and fake political information, potentially leading to an overestimation of accuracy based on their ideological beliefs (Haouari et al. 2019 ). News can be classified as genuine or fake. Moreover, we can find the multi-label datasets and the multi-class level of classification (Shahi et al. 2021 ). Fake news differs from the truth in content quality, style, and sentiment, while containing similar levels of cognitive and perceptual information (Ali et al. 2022 ; Al-Ghadir et al. 2021 ; Ayyub et al. 2021 ). Moreover, they are often matched with shorter words and longer sentences (Zhou et al. 2020 ).

Detecting fake news in Arabic presents several unique challenges compared to English. Here are some ways in which Arabic fake news detection differs:

Language structure : Arabic morphology is complex since an inflected word in the Arabic language can form a complete syntactic structure. For example, the word “فأعطيناكموه” /f > ETynAkmwh/ Footnote 1 (and we gave it to you) contains a proclitic, a verb, a subject and two objects. The linguistic complexity of the Arabic language, complex morphology and a rich vocabulary, may pose challenges for natural language processing (NLP) tasks, including fake news detection.

Dialectal variations : Even though Modern Standard Arabic (MSA) is the official language in Arab countries, many social media users use dialects to express themselves. Arabic encompasses numerous dialects across different regions, each with its vocabulary, grammar, and expressions. This diversity makes it challenging to develop models that can effectively identify fake news across various Arabic dialects. Moreover, besides the varieties of languages spoken according to the countries, the written one is also affected by code-switching which is frequent on the Web, as Internet users switch between many languages and dialects using the Web Arabic, namely Arabizi, Franco-Arabic and MSA, which makes their expressions composed of various languages. Some Arabic studies on fake news detection are aware of the presence of dialect in the tweets analyzed. By considering the dialects of North Africa and the Middle East (Ameur and Aliane 2021 ) (Yafooz et al. 2022 ), it has been proved that fake news detection systems can perform less well when dialect data is not processed (Alhindi et al. 2021 ).

Cultural nuances : Arabic-speaking communities have distinct cultural norms, beliefs, and sensitivities that influence how information is perceived and shared. Understanding these cultural nuances is essential for accurately detecting fake news in Arabic.

Data availability : English fake news detection is characterized by performing systems built on large resources and advanced approaches. The Arabic language in turn can borrow these methodologies to build systems or custom approaches for fake news detection. However, this is faced with the scarcity of its resources and its complex morphology and varieties (Nassif et al. 2022 ; Himdi et al. 2022 ; Awajan 2023 ). Compared to English, there is relatively less labeled data available for training fake news detection models in Arabic. This scarcity of data makes it challenging to develop robust and accurate detection algorithms.

Socio-political context : The socio-political landscape in Arabic-speaking regions differs from that of English-speaking countries. Fake news may serve different purposes and target different socio-political issues, requiring tailored approaches for detection.

In summary, Arabic fake news detection requires specialized techniques that account for the language's unique characteristics, dialectal variations, cultural nuances, data availability, and socio-political context. Building effective detection systems in Arabic necessitates interdisciplinary collaboration and a deep understanding of the language and its socio-cultural context. This raises the need for thorough studies to address Arabic fake news detection.

In the following, we define our research methodology. We then delineate the terminologies pertinent to fake news and its processes, providing illustrative examples to aid comprehension, particularly within the context of the Arabic language. We explore the interplay between fake news and public opinion orientation, highlighting overlapping domains and key challenges in detection. Representative datasets and their applications in various studies are outlined, with Arabic annotation samples to illustrate label assignment considerations based on language, context, topic, and information dissemination. We delve into the preprocessing steps, emphasizing the unique characteristics of the Arabic language. Additionally, we discuss the potential features extractable from shared tasks, presenting their implications and main findings. Finally, we address open issues in fake news detection, proposing avenues for future research, including dataset enhancement, feature extraction refinement, and increased awareness to mitigate fake news proliferation.

2 Research methodology

In this section, we define the main research questions based on which our study is performed. Then, we describe the whole research process and we discuss the scope of our research.

2.1 Research questions

We established a set of questions to address the purpose of our research. They range from broad to more specific questions that help in describing, defining and explaining the main aspects of a fake news detection system.

RQ1 : What is fake news and how it does affect people and society?

RQ2 : What are the criteria for a fake news detection process?

RQ3 : What are the main sources from which data are extracted?

RQ4 : What are the main annotations for the retrieved claims?

RQ5 : How to create a pertinent model for detecting fake news?

RQ6 : Are automatic or manual detection of fake news sufficient regarding the large spread of information?

RQ7 : How to prevent the spread of fake news?

We base the research process on the established questions. During this process, we aim to select papers that discuss Arabic fake news detection.

2.2 Search process

Since we are looking for relevant papers in the domain of Arabic fake news detection, we started by querying Google Scholar using ("Fake" OR "misinformation" OR "disinformation" OR "deception" OR "satirical hoaxes" OR "serious fabrication" OR "clickbait" OR "information pollution" OR "deceptive content" OR "rumors" OR "propaganda") AND ("Arabic" OR "Language"). Applying these search terms resulted in a large number of articles from which we selected those that contained relevant information. Indeed, we have used exclusion criteria to keep only those that align with the scope of our research. We thus collected 75 articles. The search process and the covered aspects are depicted in Fig. 1 .

Search process

2.3 Scope of the study

After selecting the articles that align with the scope of our research, we attempted to answer the previous research questions (RQs). Among these articles, some authors constructed the datasets and corpora used in their research, detailing the various stages of data construction. Others utilized existing datasets and applied diverse machine learning techniques, including classical methods, deep neural networks, and transformers. Additionally, certain articles focused on strategies to curb the dissemination of fake news.

The general framework of fake news detection and related components are depicted in Fig. 2 . The construction of basic knowledge represents the main step in the development of a fake news detection system. It requires careful source selection and definition of annotation levels. The multiple annotation levels are helpful to deal with variations in the style of claims. Moreover, the detection model addressed the main corpus characteristics and studied its usefulness when generalizing its application. Awareness techniques, in turn, have been described to make people aware of fake news.

General framework of our study

In the following, we define the fake news terminology, and we present the fake news detection processes by describing their approaches and illustrating them with examples.

3 Fake news terminology

Fake news is a common term employed to describe the fake content spread on the Web (Saadany et al. 2020 ). Digital communication has generated a set of concepts related to fake news that can be used interchangeably, namely misinformation, disinformation, deception, satirical hoaxes, serious fabrication, clickbait, information pollution, and deceptive content (Elsayed et al. 2019 ; Touahri and Mazroui 2018 ). They can mislead users' opinions since they include misleading information, rumors, propaganda, and techniques that influence people's mindsets (Touahri and Mazroui 2020 ; Shahi et al. 2021 ; Barron-Cedeno et al. 2020 ; Baly et al. 2018 ). The aforementioned categories differ in dependence on many factors such as targeted audience, genre, domain, and deceptive intent (Da San Martino et al. 2019 ).

The emergence of fake news on the Web has motivated domain interested researchers to perform various tasks and develop automated systems that support multiple languages (Alhindi et al. 2021 ) in order to detect fake news and prevent its disastrous effects from occurring. Among these tasks, we have:

Check-worthiness that determines whether a claim is check-worthy (Haouari et al. 2019 ). It is a ranking task where the systems are asked to produce sentence scores according to check-worthiness. Checkworthiness is the first step in determining the relevance of a claim to be checked.

Stance detection is a fake news detection subtask that searches documents for evidence and defines the documents that support the claim and those that contradict it (Touahri and Mazroui 2019 ; Ayyub et al. 2021 ). Stance detection aims to judge a claim's factuality according to the supporting information. Related information can be annotated as discuss, agree or disagree with a specific claim. Stance detection differs from fake news detection in that it is not for veracity but consistency. Thus, stance detection is insufficient to predict claim veracity since a major part of documents may support false claims (Touahri and Mazroui 2019 ; Elsayed et al. 2019 ; Alhindi et al. 2021 ; Hardalov et al. 2021 ).

Fact-checking is a task that assesses the public figures and the truthfulness of claims Khouja ( 2020 ). A claim is judged trustful or not based on its source, content and spreader credibility. Factuality detection identifies whether a claim is fake or true. The terms genuine, true, real and not fake can be used interchangeably.

Sentiment analysis is the emotion extraction task, such as customer reviews of products. The task is not to do a claim objective verification but it aims to detect opinions to not be considered facts and hence prevent their misleading effects (Touahri and Mazroui 2018 , 2020 ; Saeed et al. 2020 , 2021 ; Ayyub et al. 2021 ).

We exemplify the concepts using the statement "حماية أجهزة أبل قوية بحيث لا تتعرض للفيروسات" (Protection for Apple devices is strong so that they are not exposed to viruses). In Table 1 , the first sentence aligns with the claim, while the second contradicts it. Specifically, "قوية" contradicts "ليست قوية" and " لا تتعرض " contrasts with " تتعرض ". Consequently, when the FactChecking system encounters conflicting sentences, it labels the claim as false; otherwise, it deems it as true.

There are several steps to detect fake news that were covered by Barrón-Cedeno et al. (Barrón-Cedeño et al. 2020 ) discussed various tasks such as determining the check-worthiness of claims as well as their veracity. Stance detection between a claim-document pair (supported, refuted, not-enough-information); (agree, disagree, discuss, unrelated) has been studied (Baly et al. 2018 ) as well as defining claim factuality as fake or real (Ameur and Aliane 2021 ).

4 Challenges

The old-fashioned manual rhythm to detect fake news cannot be kept by fact-checkers regarding the need for momentary detection of claims veracity (Touahri and Mazroui 2019 ). Truth often cannot be assessed by computers alone, hence the need for collaboration between human experts and technology to detect it. Automatic fake news detection is technically challenging for several reasons:

Data diversity : Online information is diverse since it covers various subjects, which complicates the fake news detection task (Khalil et al. 2022 ; Najadat et al. 2022 ). The data may come from different sources and domains, which may complicate their processing (Zhang and Ghorbani 2020 ). The Arabic language can also be considered a criterion when dealing with its complex morphology.

Momentary detection : Fake news is written to deceive readers. It spreads rapidly and its generation mode varies momentarily, making existing detection algorithms ineffective or inapplicable. To improve information reliability, systems to detect fake news in real time should be built (Brashier et al. 2021 ). Momentary detection of fake news on social media seeks to identify them on newly emerged events. Hence, one cannot rely on news propagation information to detect fake news momentarily as it may not exist. Most of the existing approaches that learn claim-specific features can hardly handle the challenge of detecting newly emerged factuality since the features cannot be transferred to unseen events (Haouari et al. 2021 ).

Lack of information context: The information context is important to detect fake news (Himdi et al. 2022 ). In some cases, retrieving information context is not evident since it requires a hard research process to find the context and real spreader. Moreover, data extraction ethics may differ from one social media to another, which may affect the data sufficiency to detect fake news.

Misinformation: Sometimes fake information is spread by web users unintentionally, and based on their credibility fake news may be considered true (Hardalov et al. 2021 ; Sabbeh and Baatwah 2018 ).

An example of fake news is depicted in Fig. 3 . An account owner denies the claim spread by The Atlas Times page on Twitter. The post has many likes and retweets besides some comments that support it by ironing the predicate. The predicate can therefore be considered false.

Example of the spread of Arabic fake news

In this section, we delve into the datasets curated for Arabic fake news detection. We provide illustrative examples of annotated tweets from prior investigations alongside the methods used for their annotation. Subsequently, we outline their sources, domains, and sizes. Additionally, we explore the research endeavors that have utilized these datasets (Table 2 ).

Given the limited availability of resources for Arabic fake news detection, numerous studies have focused on developing linguistic assets and annotating them using diverse methodologies, including manual, semi-supervised, or automatic annotation techniques.

5.1 Manual annotation

The study (Alhindi et al. 2021 ) presented AraStance, an Arabic Stance Detection dataset of 4,063 news articles that contains true and false claims from politics, sports, and health domains, among which 1642 are true. Each claim–article pair has a manual stance label either agree, disagree, discuss, or unrelated. Khouja (Khouja 2020 ) constructed an Arabic News Stance (ANS) corpus related to international news, culture, Middle East, economy, technology, and sports; and was collected from BBC, Al Arabiya, CNN, Sky News, and France24. The corpus is labeled by 3 to 5 annotators who selected true news titles and generated fake/true claims from them through crowdsourcing. The corpus contains 4,547 Arabic News annotated as true or false, among which 1475 are fake. The annotators have used the labels paraphrase, contradiction, and other/not enough information to associate 3,786 pairs with their evidence. (Himdi et al. 2022 ) have introduced an Arabic fake news articles dataset for different genres composed through crowdsourcing. An Arabic dataset related to COVID-19 was constructed (Alqurashi et al. 2021 ). The tweets are labeled manually as containing misinformation or not. The dataset contains 1311 misinformation tweets out of 7,475. The study (Ameur and Aliane 2021 ) presented the manually annotated multi-label dataset “AraCOVID19-MFH” for fake news and hate speech detection. The dataset contains 10,828 Arabic tweets annotated with 10 different labels which are hate, Talk about a cure, Give advice, Rise moral, News or opinion, Dialect, Blame and negative speech, Factual, Worth Fact-Checking and contains fake information. The corpus contains 459 tweets labeled as fake news; whereas, for 1,839 tweets the annotators were unable to decide which tag to affect. Ali et al. (Ali et al. 2021 ) introduced AraFacts, a publicly available Arabic dataset for fake news detection. The dataset collected from 5 Arabic fact-checking websites consists of 6,222 claims along with their manual factual labels as true or false.

Information such as fact-checking article content, topics, and links to web pages or posts spreading the claim are also available. In order to target the topics most concerned by rumors, Alkhair et al. (Alkhair et al. 2019 ) constructed a fake news corpus that contains 4,079 YouTube information related to personalities deaths which gave 3435 fake news after their annotation based on keywords and pretreatment, among which 793 are rumor. Al Zaatari et al. (Al Zaatari et al. 2016 ) constructed a dataset that contains a total of 175 blog posts with 100 posts annotated as credible, 57 as fairly credible, and 18 as non-credible. There are 1570 tweets related to these posts manually annotated as credible out of 2708. Haouari et al. (Haouari et al. 2021 ) introduced ArCOV19-Rumors an Arabic Twitter dataset for misinformation detection composed of 138 verified claims related to COVID-19. The 9,414 relevant tweets to those claims identified by the authors were manually annotated by veracity to support research on misinformation detection, which is one of the major problems faced during a pandemic. Among the annotated tweets 1,753 are fake, 1,831 true and 5,830 others. ArCOV19-Rumors covers many domains politics, social, entertainment, sports, and religious. Besides the aforementioned annotation approaches, data true content can be manually altered to generate fake claims about the same topic Khouja ( 2020 ).

5.2 Semi-supervised and automatic annotation

Statistical approaches face limitations due to the absence of labeled benchmark datasets for fake news detection. Deep learning methods have shown superior performance but demand large volumes of annotated data for model training. However, online news dynamics render annotated samples quickly outdated. Manual annotation is costly and time-intensive, prompting a shift towards automatic and semi-supervised methods for dataset generation. To bolster fact-checking systems, fake news datasets are automatically generated or extended using diverse approaches, including automatic annotation. In this respect, various papers presented their approaches. In (Mahlous and Al-Laith 2021 ), there was a reliance on the France-Press Agency and the Saudi Anti-Rumors Authority fact-checkers to extract a corpus that was manually annotated into 835 fake and 702 genuine tweets. Then an automatic annotation was performed based on the best performing classifier. Elhadad et al. (Elhadad et al. 2021 ) automatically annotated the bilingual (Arabic/English) COVID-19-FAKES Twitter dataset using 13 different machine learning algorithms and employing 7 various feature extraction techniques based on reliable information from different official Twitter accounts. The authors (Nakov et al. 2021 ) have collected 606 Arabic and 2,589 English Qatar fake tweets about COVID-19 vaccines. They have analyzed the tweets according to factuality, propaganda, harmfulness, and framing. The automatic annotation of the Arabic tweets gave 462 factual and 144 not. The study (Saadany et al. 2020 ) introduced datasets concerned with political issues related to the Middle East. A dataset that consists of fake news contains 3,185 articles scraped from ‘Al-Hudood’ and ‘Al-Ahram Al-Mexici’ Arabic satirical news websites. They also collected a dataset from ‘BBC-Arabic’, ‘CNN-Arabic’ and ‘Al-Jazeera news’ official news sites. The dataset consists of 3,710 real news articles. The websites from which data has been scraped are specialized in publishing true and fake news. Nagoudi et al. (Nagoudi et al. 2020 ) have presented AraNews, a POStagged news dataset. The corpus was constructed based on a novel method for the automatic generation of Arabic manipulated news based on online news data as seeds for the generation model. The dataset contains 10,000 articles annotated using true and false tags. Moreover, Arabic fake news can be generated by translating fake news from English into Arabic (Nakov et al. 2018 ).

We summarize in Table 3 the main datasets by specifying their sources, their sizes, the domains concerned, the tags adopted, and the labeling way.

6 Fake news detection

6.1 preprocessing.

The content posted online is often chaotic and marked by considerable ambiguity. Therefore, before proceeding to feature extraction, it is imperative to conduct a preprocessing phase. Below, we outline the general procedures along with those tailored specifically for the Arabic language:

General steps

Special characters removal : special characters such as {∗ ,@,%,&…} are not criteria to detect fake news and are not specific to a language, their removal helps to clean the text (Alkhair et al. 2019 ).

Punctuation removal: they are considered non significant to detect fake news (Al-Yahya et al. 2021 ).

URL links removal : their presence in the raw text may be considered noise. However, they may represent a page with important content (Alkhair et al. 2019 ).

Duplicated comments removal : the retweet or duplicate comments have to be deleted since it is sufficient to process a piece of text just once (Alkhair et al. 2019 ).

Balancing data : data imbalance can mislead the classification process. Therefore, it is essential to balance the data to represent each factual or false class equally (Jardaneh et al. 2019 ).

Reducing repeated letters, characters and multiple spaces (Al-Yahya et al. 2021 ): since letters are not repeated more than twice in a word, nor are spaces that are unique between words in a sentence, it is essential to eliminate these repetitions to achieve the correct form of a word or a sentence.

Tokenization helps in splitting sentences into word sequences using delimiters such as space or punctuation marks. This step precedes converting texts into features (Oshikawa et al. 2018 ) .

Stemming, lemmatization, and rooting are language related steps that help to cover a large set of words by representing them with their common stems, lemmas and roots (Oshikawa et al. 2018 ).

Normalization and standardization : Normalizing data giving them the same representation. In the Arabic language, some letters may be replaced with others (Jardaneh et al. 2019 ).

Arabic specific steps

Foreign language words removal: they don’t belong to the processed language (Alkhair et al. 2019 ).

Non-Arabic letter removal: the transliterated text can be removed since it is lowly represented within the studied corpora (Alkhair et al. 2019 ).

Replacing hashtags and emojis with relevant signification (Al-Yahya et al. 2021 ): for example ☺ may be replaced with سعيد.

Removing stop words: stop words such as أنت لكن ما are considered non significant in detecting fake news (Al-Yahya et al. 2021 ). The stop words are specific to each language.

Diacritization removal: since diacritic marks don’t cover all the terms, their suppression helps to normalize the terms representation (Al-Yahya et al. 2021 ).

6.2 Feature extraction

Previous studies on fake news detection have relied on various features extracted from labeled datasets to represent information. In this section, we detail the most commonly utilized features in fake news detection:

Source features : These check whether a specific user account is verified, determine its location, creation date, activity, user details and metadata including, the job, affiliation and political party of the user and whether the account is with a real name (Jardaneh et al. 2019 ; Sabbeh and Baatwah 2018 ). The account real name and details are important to assess the news creator credibility. Moreover, it is important to know whether the news spreader belongs to opposite parties which tend to be fake. Also, the creation date is important since an account nearly created during a specific event can be considered fake in comparison to an early created one. Source features are very helpful in determining the credibility of a news creator; However, Fake information may be spread unintentionally on account of high credibility.

Temporal features : These capture the temporal spread of information and the overlap between the posting time of user comments. Fake news may be retweeted rapidly, so it is important to capture the comment temporal information and whether it overlaps with a specific event that pushes the spreader to publish fake information. The temporal features are among the most important ones to detect fake news; however, they are still not sufficient to deal with the strength of such a phenomenon.

Content features : Content can be true if it contains pictures, hashtags or URLs since they may lead to a trustful source of information to prove the factuality of a claim. Also, if the content is retweeted by trustful accounts or has positive comments from users, it can be considered true (Sabbeh and Baatwah 2018 ). Content features cover information and their references; However, there is a need to check the references credibility.

Lexical features : These include character and word level features extracted from text (Sabbeh and Baatwah 2018 ). Analyzing claims at the term level is crucial for determining their sentiment (positive or negative) and verifying their factual accuracy. Identifying common lexical features among claims is also valuable. However, while lexical features are significant, they should be complemented with additional features to effectively identify fake claims.

Linguistic features : Analyzing the linguistic features of a claim can help determine its veracity without considering external factual information. Term frequency, bag-of-words, n-grams, POS tagging, and sentiment score are some of the main features used for fake news detection Khouja ( 2020 ). Linguistic features categorize data based on language and highlight its defining elements, relying on lexical, semantic, and structural characteristics. While linguistic features aid in content analysis and representation, the absence of contextual information can potentially lead to misidentification during fake news detection.

Semantic features : These are features that capture the semantic aspects of a text that are useful to extract data meaning (Sabbeh and Baatwah 2018 ) and their variations according to the context. Semantic features identify the meaning of a claim but not its veracity.

Sentiment features: Sentiment analysis may improve the fake news prediction accuracy (Jardaneh et al. 2019 ) since a sentimental comment may be fake as it doesn’t depend on facts. As opinions influence people’s behaviors, it has numerous applications in real life such as in politics, marketing, and social media (Ayyub et al. 2021 ). Sentiment features are important to distinguish between opinions and facts; However, we may express a fact using an opinion such as I like the high quality of Apple smartphones.

The mentioned features are complimentary, so we can’t rely on one feature without the other. Each feature has its main characteristics that make it indispensable for fake news detection.

6.3 Classification approaches

In this section, we outline various studies conducted in Arabic fake news detection, detailing the features employed, the models developed, and the achieved performances. We categorize these studies into three main approaches: those based on classical machine learning, deep learning or transformers.

6.3.1 Classical machine learning

Classical machine learning for fake news detection involves the application of traditional algorithms and techniques to analyze and classify textual data to discern between authentic and fabricated news articles. These methods typically rely on feature engineering, where relevant characteristics of the text are extracted and used to train models such as support vector machines (SVM), logistic regression (LR), decision trees (DT), and random forests (RF). Features can include linguistic patterns, sentiment analysis, lexical and syntactic features, and metadata associated with the news articles. The trained models are then employed to classify new articles as either genuine or fake based on the learned patterns and characteristics present in the data. Researchers may collect a dataset of Arabic news articles labeled as fake or genuine. They then preprocess the text, extract relevant features, and train a machine learning classifier on the labeled dataset. The classifier can then be used to predict the authenticity of new Arabic news articles. Arabic satirical news have lexico-grammatical features that distinguish them (Saadany et al. 2020 ). Based on this claim, a set of machine learning models for identifying satirical fake news has been tested. The model achieved an accuracy of up to 98.6% based on a dataset containing 3,185 fake and 3,710 real articles. (Alkhair et al. 2019 ) have used a dataset of 4,079 news, where 793 are rumors, based on which they have trained a model on 70% of the data and tested it on the remainder. They have classified comments as rumor and no rumor using the most frequent words as features and three machine learning classifiers namely, Support Vector Machine (SVM), Decision Tree (DT) and Multinomial Naïve Bayes (MNB). They attained a 95.35% accuracy rate with SVM. The researchers (Sabbeh and Baatwah 2018 ) utilized a dataset comprising 800 news items sourced from Twitter and devised a machine learning model for assessing the credibility of Arabic news. They incorporated topic and user-related features in their model to evaluate news credibility, ensuring a more precise assessment. By verifying content and analyzing user comments' polarity, they classified credibility using various classifiers, including Decision Trees. Consequently, they achieved an accuracy of 89.9%. The authors (Mahlous and Al-Laith 2021 ) extracted n-gram TF-IDF features from a dataset containing 835 fake and 702 genuine tweets, achieving an F1-score of 87.8% using Logistic Regression (LR). The authors (Thaher et al. 2021 ) have extracted a Bag of Words and features including content, user profiles, and word-based features from a Twitter dataset comprising 1,862 tweets (Al Zaatari et al. 2016 ). Their results showed that the Logistic Regression classifier with TF-IDF model achieved the highest scores compared to other models. They reduced dimensionality using the binary Harris Hawks Optimizer (HHO) algorithm as a wrapper-based feature selection approach. Their proposed model attained an F1-score of 0.83, marking a 5% improvement over previous work on the same dataset. (Al-Ghadir et al. 2021 ) evaluated the stance detection model based on the TF-IDF feature and varieties of K-nearest Neighbors (KNN) and SVM on the SemEval-2016 task 6 benchmark dataset. They reached a macro F -score of 76.45%. The authors (Gumaei et al. 2022 ) conducted experiments on a public dataset containing rumor and non-rumor tweets. They have built a model using a set of features, including topic-based, content-based, and user-based features; besides XGBoost-based approach that has achieved an accuracy of 97.18%. Jardaneh et al. (Jardaneh et al. 2019 ) have extracted 46 content and user related features from 1,862 tweets published on topics covering the Syrian crisis and employed sentiment analysis to generate new features. They have based the identification of fake news on a supervised classification model constructed based on Random Forest (RF), Decision Tree, AdaBoost, and Logistic Regression classifiers. The results revealed that sentiment analysis led to improving the prediction accuracy of their system that filters out fake news with an accuracy of 76%.

6.3.2 Deep learning

Deep neural approaches for fake news detection involve the use of deep learning models based on neural network architectures, to automatically learn and extract relevant features from textual data to distinguish genuine news articles from fabricated news articles. These approaches typically utilize neural network architectures such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and more recently, Transformer-based models like BERT and GPT. In deep neural approaches, the models are trained on large volumes of labeled data, where the neural networks learn to represent the underlying patterns and relationships within the text. These models can be used for Arabic fake news detection since they automatically learn representations of the text and capture complex patterns and relationships. Researchers may build a deep neural network architecture tailored to Arabic text data. For example, they can use a CNN for text classification, where the network learns to identify important features in the text through convolutional layers. By training the model on a large dataset of labeled Arabic news articles, it can learn to distinguish between fake and genuine news. (Yafooz et al. 2022 ) proposed a model to detect fake news about the Middle-east COVID-19 vaccine on YouTube videos. The model is based on sentiment analysis features and a deep learning approach which helped to reach an accuracy of 99%. The authors (Harrag and Djahli 2022 ) have used an Arabic balanced corpus to build their model that unifies stance detection, relevant document retrieval and fact-checking. They proposed a deep neural network approach to classify fake and real news by exploiting CNNs. The model trained on selected attributes reached an accuracy of 91%. (Alqurashi et al. 2021 ) have exploited FastText and word2vec word embedding models for more than two million Arabic tweets related to COVID-19. (Helwe et al. 2019 ) extracted various features from a dataset containing 12.8 K annotated political news statements along with their metadata. These features included content and user-related attributes. Their initial model, based on TF-IDF and SVM classifier, achieved an F1-score of 0.57. They also explored word-level, character-level, and ensemble-based CNN models, yielding F1-scores of 0.52, 0.54, and 0.50 respectively. To address the limited training data, they introduced a deep co-learning approach, a semi-supervised method utilizing both labeled and unlabeled data. By training multiple weak deep neural network classifiers in a semi-supervised manner, they achieved significant performance improvement, reaching an F1-score of 0.63.

6.3.3 Transformer-based approaches

Transformer approaches for fake news detection involve the use of transformer-based models, which are a type of deep learning architecture that has gained prominence in NLP tasks. The transformer model has become the foundation for many state-of-the-art NLP models. In the context of fake news detection, transformer-based models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) are used to analyze and classify textual data. These models can process and understand large amounts of text data by leveraging self-attention mechanisms, which allow them to capture contextual relationships between words and phrases in a given text. Transformer-based models are pre-trained on massive unlabeled corpora of text data and then fine-tuned on specific tasks such as fake news detection. During fine-tuning, the model learns to classify news articles as either genuine or fake based on the patterns and relationships it has learned from the pre-training phase. Transformer-based approaches have shown promising results in fake news detection tasks due to their ability to capture semantic meaning, context, and long-range dependencies within textual data. These models have shown remarkable performance in understanding context and semantics in text. Researchers may fine-tune pre-trained transformer models on Arabic fake news detection datasets. For instance, they can use AraBERT as a base model and fine-tune it on a dataset of labeled Arabic news articles. During fine-tuning, the model learns to effectively capture linguistic nuances and identify linguistic patterns indicative of fake news in Arabic text. The study (Nagoudi et al. 2020 ) aimed to measure the human ability to detect machine manipulated Arabic text based on a corpus that contains 10,000 articles. They reported that changing a certain POS does not automatically flip the sentence veracity. Their system for Arabic fake news detection reached an F1-score of 70.06. They have made their data and models publicly available. Khouja (Khouja 2020 ) explored textual entailment and stance prediction to detect fake news from a dataset that contains 4,547 Arabic news where 1475 are fake. They have constructed models based on pretraining BERT. Their system predicts stance by an F1-score of 76.7 and verifies claims by an F1-score of 64.3. (Al-Yahya et al. 2021 ) have compared language models based on neural networks and transformers for Arabic fake news detection from ArCOV19-Rumors (Haouari et al. 2021 ) and Covid-19-Fakes (Elhadad et al. 2021 ) datasets. They then reported that models based on transformers perform the best, achieving an F1-score of 0.95.

6.3.4 Approach distinction for Arabic fake news

We may differentiate between machine learning (ML), deep learning (DL), and transformer-based approaches in terms of their approach, capabilities, and suitability for Arabic fake news detection based on the following criteria:

Classical machine learning: ML models are effective when the features are well-defined and the dataset is not too large. They can handle relatively small datasets and are interpretable, making it easier to understand why a particular prediction was made. ML approaches are suitable for Arabic fake news detection when the features can effectively capture linguistic patterns indicative of fake news in Arabic text. They may be less effective in capturing complex semantic relationships and context compared to DL and transformer-based models.

Deep learning : DL models excel at learning hierarchical representations of data and can handle large volumes of text data. They can automatically learn features from raw text, making them suitable for tasks where feature engineering may be challenging. DL approaches are suitable for Arabic fake news detection when the dataset is large and diverse, and the linguistic patterns indicative of fake news are complex. They may outperform ML approaches in capturing subtle linguistic cues and context.

Transformer-based approaches : Transformer-based models are state-of-the-art in natural language understanding tasks and excel at capturing context and semantics in text. They can capture bidirectional relationships between words and are highly effective in capturing nuanced linguistic features. Transformer-based approaches are highly suitable for Arabic fake news detection, especially when the dataset is large and diverse. They can effectively capture complex semantic relationships and context in Arabic text, making them well-suited for tasks where understanding linguistic nuances is crucial.

In summary, classical ML approaches are suitable for Arabic fake news detection when the features can effectively capture linguistic patterns, while DL and transformer-based approaches excel at capturing complex semantic relationships and context in Arabic text, making them highly effective for detecting nuanced linguistic cues indicative of fake news. These three families of approaches can interact and complement each other in various ways:

Feature engineering and representation: ML methods often require handcrafted features extracted from the text, such as word frequencies, n-grams, and syntactic features. DL methods can automatically learn features from raw text data, making them suitable for tasks where feature engineering may be challenging. Transformer-based models, such as BERT, leverage pre-trained representations of text that capture rich semantic information. These representations can be fine-tuned for specific tasks, including fake news detection.

Model complexity and performance: ML methods are generally simpler and more interpretable compared to DL and transformer-based models. They may be suitable for tasks where transparency and interpretability are important. DL methods, with their ability to learn hierarchical representations of data, can capture complex patterns and relationships in the text. They may outperform ML methods on tasks that require understanding subtle linguistic cues and context. Transformer-based models, with their attention mechanisms and contextual embeddings, have achieved state-of-the-art performance on various NLP tasks, including fake news detection. They excel at capturing fine-grained semantic information and context.

Ensemble learning: ML, DL, and transformer-based models can be combined in ensemble learning approaches to leverage the strengths of each method. Ensemble methods combine predictions from multiple models to make a final prediction. This can lead to improved performance and robustness, especially when individual models have complementary strengths and weaknesses. In (Noman Qasem et al. 2022 ), several standalone and ensemble machine learning methods were applied to the ArCOV-19 dataset that contains 1480 Rumors and 1677 non-Rumors tweets based on which they have extracted user and tweet features. The experiments showed an interesting accuracy of 92.63%.

Progression and evolution: There is a progression from traditional ML methods to more advanced DL and transformer-based approaches in NLP tasks, including fake news detection. As the field of NLP continues to evolve, researchers are exploring novel architectures, pre-training techniques, and fine-tuning strategies to improve the performance of models on specific tasks, such as fake news detection.

In practice, these approaches are often used in parallel, with researchers and practitioners selecting the method or combination of methods that best suit the task requirements, data characteristics, and computational resources available. The choice of approach may depend on factors such as dataset size, complexity of linguistic patterns, interpretability requirements, and performance goals.

We recall in Table 4 the various studies carried out on the detection of Arab fake news. The conducted studies employed various datasets, features, models, and evaluation metrics. The primary metrics used include Accuracy, Precision, Recall, F1-score, and AUC score. These studies aimed to identify fake news by utilizing diverse approaches, ranging from classical machine learning algorithms to deep learning models.

From Table 4 , a wide range of accuracies achieved by the system, spanning from 76% to over 99%, attributable to differences in datasets and underlying knowledge bases. However, a pertinent question arises when applying the best-performing models to other datasets, often resulting in reduced accuracy. To address this issue and facilitate a fair comparison of proposed approaches, some shared task organizers have made publicly available datasets and proposed tasks. These initiatives aim to mitigate model sensitivity to training data and enhance overall system efficiency.

7 Fake news shared tasks

The organizers of the competition CLEF–2019 CheckThat! Lab (Elsayed et al. 2019 ) proposed task revolves around the Automatic Verification of Claims, as presented by CheckThat! that outlines two primary tasks. The first task focuses on identifying claim fact-check worthiness within political debates. Meanwhile, the second task follows a three-step process. The initial step involves ranking web pages according to their utility for fact-checking a claim. The systems achieved an nDCG@10 lower than 0.55 which is the original ranking in the search result list considered as baseline. The second step classifies the web pages according to their degree of usefulness, the best performing system reached an F1 of 0.31, and the third task extracts useful passages from the useful pages, in which the most occurring model reached an F1 of 0.56. The second step is designed to help automatic fact-checking represented in the fourth task that uses the useful pages to predict a claim factuality in the system that used textual entailment with embedding-based representations for classification has reached the best F1 performance measured by 0.62. The task organizers have released datasets in English and Arabic in order to enable the research community in checkworthiness estimation and automatic claim verification. CheckThat! Lab 2021 task (Shahi et al. 2021 ) focuses on multi-class fake news detection. The lab covers Arabic, English, Spanish, Turkish, and Bulgarian. The best performing systems achieved a macro F1-score between 0.84 and 0.88 in the English language. The paper (Al-Qarqaz et al. 2021 ) describes NLP4IF, the Arabic shared task to check COVID-19 disinformation. The best ranked model for Arabic is based on transformer-based pre-trained language, an ensemble of AraBERT-Base, Asafya-BERT, and ARBERT models and achieved an F1-Score of 0.78. The authors (Rangel et al. 2020 ) have presented an overview of Author Profiling shared task at PAN 2020. The best results have been obtained in Spanish with an accuracy of 82% using combinations of character and word n-grams; and SVM. The task has focused on identifying potential spreaders of fake news based on the authors of Twitter comments, highlighting challenges related to the lack of domain specificity in news. It attracted 66 participants, whose systems were evaluated by the organizers of the task.

8 Discussion

The Arabic fake news detection systems have achieved satisfactory results. However, given the ongoing generation of content, dataset quality struggles to encompass the diversity of generated content. Datasets vary in size, sources, and the hierarchical annotation steps used to detect fake news. Human annotation remains challenging, as multiple aspects must be considered before labeling a claim based on its content. Therefore, semi-supervised and automatic annotation methods have been explored to alleviate the burden of manual annotation.

Detecting fake news requires further effort to be successful, especially in terms of real-time detection, which remains challenging due to the absence of comprehensive detection aspects such as information spread. For instance, information shared by a reputable individual may be perceived as true. Improving public literacy is crucial since individuals need to be educated to discern factual content from misinformation.

The spread of fake news may have disastrous effects on people and society, hence, the detection step must be taken before allowing the spread of data especially on social media which is characterized by wide sets of data. Moreover, social media users have to agree to ethical aspects, and punishments have to be applied to those who spread fake data. Trustful sources must be explored when seeking factual information. The exploration of new models may be useful also. It can be an approach that relies on scoring social media users based on their trustworthiness that pops up every time a new post is created. The score is decreased each time until a specific account is marked as untrustworthy, which can either help prevent the spread of fake information or destroy its base account. Special information may be requested when creating an account in order to not allow a specific person to create more than one account.

In the following, we describe some future directions that may be helpful in detecting fake news and preventing the spread of its negative effects.

9 Future directions for Arabic fake news detection

Researchers have employed a variety of features, including source, context, and content, to enhance fake news detection. Source features aid in targeting analysis, often complemented by content features for improved accuracy. Linguistic analysis has played a role in identifying content characteristics, with lexical and semantic features helping to identify relevant terms such as sentiment. Temporal features capture data spread and event relationships, though content features may lose effectiveness across different contexts. Sentiments alone may not reliably indicate fake news, as they can accompany both genuine and fake information. Additionally, the absence of typos may signal attackers' efforts to enhance content credibility. Profile and graph-based features, used to assess source credibility based on network connections, can provide valuable information for attackers to strategize long-term attacks.

The presented data and results inspire motivation to detail open issues of fake news detection. Consequently, there is a need for potential research tasks that:

Differentiate fake news from other related concepts based on content, intention and authenticity;

Enhance content features by non-textual data;

Investigate the importance of the automatically annotated corpora, lexical features, hand-crafted rules and pretrained models with the aim to facilitate fake news detection and improve its accuracy;

Analyze in depth the performance of current fake news detection models, and at what level their accuracy remains by varying the fields of application or the attack manners,

Improve the detection by adding pertinent features since old ones can be exploited by attackers to make users believe that fake news is true,

Propose new techniques to raise Internet users' awareness of fake news and the devastating effect of this phenomenon.

The aforementioned points highlight some directions and open issues for fake news detection. Besides these common points with other languages, the Arabic language is faced with its dialectal varieties and its complex morphology, which reflect its challenging nature. It is therefore important to explore in future research on Arab fake news these points from different angles to improve existing detection approaches and results.

Arabic can also benefit from studies on other languages to create and expand datasets, improve annotation and classification models in addition to improving customized fake news awareness techniques.

9.1 Datasets

The Arabic datasets are mainly related to politics and the COVID-19 pandemic that has emerged recently. Hence, as further studies, the Arabic language can benefit from foreign language datasets either by translation or collection manner. In this context, many datasets can be explored since they are characterized by a considerable size and variety of domains. Wang ( 2017 ) manually labeled LIAR that contains 12,800 English short statements about political statements in the U.S related to various domains among which elections, economy, healthcare, and education. (Sahoo and Gupta 2021 ; Zhang et al. 2020 ; Shu et al. 2017 ; Kaur et al. 2020 ; Wang et al. 2020 ) datasets are related to English politics and their size varies between 4,048 and 37 000 tweets. The datasets of tweets presented in (Shu et al. 2017 ; Karimi et al. 2018 ) have a considerable size that exceeds 22,140 news articles related to politics, celebrity reports, and entertainment stories. Moreover, Arabic studies need to explore the balance of datasets to reduce the error of differentiating between fake and genuine news (Jones-Jang et al. 2021 ). They should also explore multimodal fake news detection (Haouari et al. 2021 ).

The training of deep learning models requires a large amount of annotated data. Moreover, due to the dynamic nature of online news, annotated samples may become outdated quickly which makes them non-representative of newly emerged events. Manual annotation can’t be the ultimate annotation manner since it is expensive and time-consuming. Hence, automatic and semi-supervised approaches have to be used to generate labeled datasets. To increase the robustness of fact-checking systems, the available fake news datasets can be generated or extended automatically based on various approaches. Among these the ones based on Generative Enhanced Model (Niewinski et al. 2019 ) or reinforced weakly supervised fake news detection approaches (Wang et al. 2020 ) and the alteration of genuine content to generate fake claims about the same topic Khouja ( 2020 ).

Improving existing datasets for Arabic fake news detection involves several strategies aimed at enhancing the quality, diversity, and representativeness of the data. Here are some ways to improve existing datasets:

Data annotation and labeling : Invest in rigorous and consistent annotation and labeling processes to ensure accurate classification of news articles as fake or genuine. Use multiple annotators to mitigate bias and improve inter-annotator agreement. Include diverse perspectives and expertise in the annotation process to capture nuances in fake news detection.

Data augmentation : Augment existing datasets by generating synthetic examples of fake news articles using techniques such as back-translation, paraphrasing, and text summarization. This can help increase the diversity of the dataset and improve model generalization.

Balancing class distribution : Ensure that the dataset has a balanced distribution of fake and genuine news articles to prevent classifier bias towards the majority class. Use techniques such as oversampling, undersampling, or synthetic sampling to balance class distribution and improve classifier performance.

Multimodal data integration : Integrate additional modalities such as images, videos, and metadata (e.g., timestamps, sources) into the dataset to provide richer contextual information for fake news detection. Multimodal datasets can capture subtle cues and patterns that may not be apparent in text alone.

Fine-grained labeling : Consider incorporating fine-grained labels or sub-categories of fake news (e.g., clickbait, propaganda, satire) to provide more detailed insights into the nature and characteristics of fake news articles. Fine-grained labeling can enable more nuanced analysis and model interpretation.

Cross-domain and cross-lingual datasets : Collect and incorporate data from diverse domains and languages to improve model robustness and generalization. Cross-domain and cross-lingual datasets expose models to a wider range of linguistic and contextual variations, enhancing their ability to detect fake news across different domains and languages.

Continuous updating and evaluation : Regularly update and evaluate existing datasets to reflect evolving trends, emerging fake news techniques, and changes in language use. Incorporate feedback from users and domain experts to iteratively improve dataset quality and relevance.

Open access and collaboration : Foster an open-access culture and encourage collaboration within the research community to share datasets, tools, and resources for fake news detection. Open datasets facilitate reproducibility, benchmarking, and model comparison, leading to advancements in the field.

Ethical considerations: Adhere to ethical guidelines and data privacy regulations when collecting and using data, ensuring the protection of individuals' privacy and rights.

By implementing these strategies, researchers and practitioners can enhance the quality and effectiveness of existing datasets for Arabic fake news detection, leading to more robust and reliable detection models.

9.2 Feature extraction

Many features should be explored to develop more sophisticated linguistic and semantic features specific to Arabic language characteristics, including morphology, syntax, and semantics. Indeed, the analysis of the source-credibility features, the number of authors, their affiliations, and their history as authors of press articles can play an important role in fake news detection. Additionally, word count, lexical, syntactic and semantic levels, discourse-level news sources (Shu et al. 2020 ; Elsayed et al. 2019 ; Sitaula et al. 2020 ), as well as Publishing Historical Records (Wang et al. 2018 ) can also contribute to the detection of fake news. The temporal features and hierarchical propagation network on social media must be explored (Shu et al. 2020 ; Ruchansky et al. 2017 ). The studies can be enhanced by the extraction of event-invariant features (Wang et al. 2018 ).

9.3 Classification

Besides the existing classification approaches, the Arabic models have to be domain and content nature aware. Improving existing models for Arabic fake news detection can involve the following approaches:

Model architecture enhancement : Explore advanced neural network architectures and techniques tailored to handle Arabic text, by enhancing attention mechanisms, and memory networks, and enlarging the size of the existing pretrained models to increase the fake news detection systems (Khan et al. 2021 ; Ahmed et al. 2021 ).

Multimodal learning : Incorporate multimodal information, such as images, videos, and metadata, in addition to textual content, to improve the model's understanding and detection of fake news.

Semi-supervised learning : Leverage semi-supervised learning techniques to make more efficient use of limited labeled data by combining it with a large amount of unlabeled data, which is often abundant in real-world scenarios.

Domain adaptation : Investigate domain adaptation methods to transfer knowledge learned from other languages or domains to improve model performance on Arabic fake news detection tasks. Exploring multi-source, multi-class and multi-lingual Fake news Detection (Karimi et al. 2018 ; Wang 2017 ).

Ensemble methods : Combine predictions from multiple models or model variants to enhance the robustness and generalization ability of the overall system.

Continuous evaluation and updating : Regularly evaluate model performance on new data and fine-tune the model parameters or architecture based on feedback to ensure adaptability to evolving fake news detection challenges.

9.4 Fake news awareness techniques

Researchers have investigated the repercussions of fake news on various fronts, proposing methods to counter its influence without relying solely on identification systems. They advocate for raising awareness among individuals and propose alternative detection strategies. To summarize, the awareness techniques encompass the following points:

Investigating the influence of culture and demographics on the fake news spread via social media since culture has the most significant impact on the spread of fake news (Rampersad and Althiyabi 2020 ).

Studying fake news impact on consumer behavior by performing an empirical methodological approach (Visentin et al. 2019 ) and identifying the key elements of fake news that is misleading content that intends to cause reputation harm (Jahng et al. 2020 ).

Sensitizing adults since they are the most targeted by fake news as they share the most misinformation, and this phenomenon could intensify in years to come (Rampersad and Althiyabi 2020 ; Brashier and Schacter 2020 ).

Boosting resilience to misinformation, which may make people more immune to misinformation (Lewandowsky and van der Linden 2021 ).

Increasing fake news identification by helping the rise of information literacy (Jones-Jang et al. 2021 ).

Preventing misinformation on the widespread adoption of health protective behaviors in the population (Yafooz et al. 2022 ), in particular for COVID-19.

Improving the ability to spot misinformation by introducing online games to detect fake news (Basol et al. 2020 ).

10 Conclusion

This survey was structured to help researchers in the field to define their roadmaps based on the proposed and presented information. Indeed, we presented the terminologies related to automatic fake news detection. We highlighted the impact of fake news on the public opinion orientation and the importance of the distinction between facts and opinions. Then, we presented recent Arabic benchmark datasets and addressed the potentially extracted features along with their categories. We described various studies and hence several approaches and experimental results. We have then compared each system results and proposed new recommendations for future approaches. Based on the compiled findings, fake news detection continues to confront numerous challenges, with ample opportunities for enhancement across various facets including feature extraction, model development, and classifier selection. Addressing the open issues and future research directions in fake news detection involves distinguishing between fake news and related concepts like satire, as well as identifying check-worthy content within extensive datasets. The construction of pre-trained models that are invariant to domains, topics, and source or language changes also represents a challenge to be met. Moreover, the construction of models for the detection of newly emergent data to which the system is not accustomed is strongly recommended. Furthermore, the systems must be able to explain what news are fake or true to enhance the existing models. While our survey comprehensively covers contemporary aspects of fake news detection, its scope is constrained by the dynamic nature of fake news research, preventing us from incorporating real-time updates on research advancements.

http://www.qamus.org/transliteration.htm

Ahmed B, Ali G, Hussain A, Baseer A, Ahmed J (2021) Analysis of text feature extractors using deep learning on fake news. Eng Technol Appl Sci Res 11:7001–7005. https://doi.org/10.48084/etasr.4069

Article Google Scholar

Al Zaatari A, El Ballouli R, ELbassouni S, El-Hajj W, Hajj H, Shaban K, Habash N, Yahya E (2016) Arabic corpora for credibility analysis. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16) (pp 4396–4401)

Al-Ghadir AI, Azmi AM, Hussain A (2021) A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments. Inf Fusion 67:29–40. https://doi.org/10.1016/j.inffus.2020.10.003

Alhindi T, Alabdulkarim A, Alshehri A, Abdul-Mageed M, Nakov P (2021) AraStance: a multi-country and multi-domain dataset of arabic stance detection for fact checking. ArXiv210413559 Cs

Ali K, Li C, Muqtadir SA (2022) The effects of emotions, individual attitudes towards vaccination, and social endorsements on perceived fake news credibility and sharing motivations. Comput Hum Behav 134:107307

Ali ZS, Mansour W, Elsayed T, Al‐Ali A (2021) AraFacts: the first large Arabic dataset of naturally occurring claims. In Proceedings of the sixth Arabic natural language processing workshop (pp 231–236)

Alkhair M, Meftouh K, Smaïli K, Othman N (2019) An Arabic corpus of fake news: collection, analysis and classification. In: Smaïli K (ed) Arabic language processing: from theory to practice, communications in computer and information science. Springer International Publishing, Cham, pp 292–302. https://doi.org/10.1007/978-3-030-32959-4_21

Chapter Google Scholar

Al-Qarqaz A, Abujaber D, Abdullah MA (2021) R00 at NLP4IF-2021 fighting COVID-19 infodemic with transformers and more transformers. In: Proceedings of the fourth workshop on NLP for internet freedom: censorship, disinformation, and propaganda, online. pp 104–109. https://doi.org/10.18653/v1/2021.nlp4if-1.15

Alqurashi S, Hamoui B, Alashaikh A, Alhindi A, Alanazi E (2021) Eating garlic prevents COVID-19 infection: detecting misinformation on the Arabic content of twitter. ArXiv210105626 Cs

Al-Yahya M, Al-Khalifa H, Al-Baity H, AlSaeed D, Essam A (2021) Arabic fake news detection: comparative study of neural networks and transformer-based approaches. Complexity 2021:1–10. https://doi.org/10.1155/2021/5516945

Ameur MSH, Aliane H (2021) AraCOVID19-MFH: Arabic COVID-19 multi-label fake news and hate speech detection dataset. ArXiv210503143 Cs

Awajan ALBARA (2023) Enhancing Arabic fake news detection for Twitters social media platform using shallow learning techniques. J Theor Appl Inf Technol 101(5):1745–1760

Ayyub K, Iqbal S, Nisar MW, Ahmad SG, Munir EU (2021) Stance detection using diverse feature sets based on machine learning techniques. J Intell Fuzzy Syst 40(5):9721–9740

Baly R, Mohtarami M, Glass J, Màrquez L, Moschitti A, Nakov P (2018) Integrating stance detection and fact checking in a unified corpus. arXiv preprint arXiv:1804.08012

Barron-Cedeno A, Elsayed T, Nakov P, Martino GDS, Hasanain M, Suwaileh R, Haouari F, Babulkov N, Hamdan B, Nikolov A, Shaar S, Ali ZS (2020) Overview of CheckThat! 2020: automatic identification and verification of claims in social media. ArXiv200707997 Cs

Barrón-Cedeño A, Elsayed T, Nakov P, Da San Martino G, Hasanain M, Suwaileh R, Haouari F, Babulkov N, Hamdan B, Nikolov A, Shaar S (2020) Overview of CheckThat! 2020: automatic identification and verification of claims in social media. In: International conference of the cross-language evaluation forum for European languages. Springer, Cham, pp 215–236

Basol M, Roozenbeek J, Van der Linden S (2020) Good news about bad news: gamified inoculation boosts confidence and cognitive immunity against fake news. J Cogn 3:2. https://doi.org/10.5334/joc.91

Bovet A, Makse HA (2019) Influence of fake news in Twitter during the 2016 US presidential election. Nat Commun 10:7. https://doi.org/10.1038/s41467-018-07761-2

Brashier NM, Schacter DL (2020) Aging in an era of fake news. Curr Dir Psychol Sci 29:316–323. https://doi.org/10.1177/0963721420915872

Brashier NM, Pennycook G, Berinsky AJ, Rand DG (2021) Timing matters when correcting fake news. Proc Natl Acad Sci 118:e2020043118. https://doi.org/10.1073/pnas.2020043118

Da San Martino G, Seunghak Y, Barrón-Cedeno A, Petrov R, Nakov P (2019) Fine-grained analysis of propaganda in news article. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP). Association for Computational Linguistics, pp 5636–5646

Elhadad MK, Li KF, Gebali F (2021) COVID-19-FAKES: a twitter (Arabic/English) dataset for detecting misleading information on COVID-19. In: Barolli L, Li KF, Miwa H (eds) Advances in intelligent networking and collaborative systems, advances in intelligent systems and computing. Springer International Publishing, Cham, pp 256–268. https://doi.org/10.1007/978-3-030-57796-4_25

Elsayed T, Nakov P, Barrón-Cedeno A, Hasanain M, Suwaileh R, Da San Martino G, Atanasova P (2019) Overview of the CLEF-2019 CheckThat! Lab: automatic identification and verification of claims. In Experimental IR Meets Multilinguality, Multimodality, and Interaction: 10th International Conference of the CLEF Association, CLEF 2019, Lugano, Switzerland, September 9–12, 2019, Proceedings 10. Springer International Publishing, pp 301–321

Gumaei A, Al-Rakhami MS, Hassan MM, De Albuquerque VHC, Camacho D (2022) An effective approach for rumor detection of arabic tweets using extreme gradient boosting method. ACM Trans Asian Low-Resour Lang Inf Process 21:1–16. https://doi.org/10.1145/3461697

Haouari F, Ali ZS, Elsayed T (2019) bigIR at CLEF 2019: automatic verification of arabic claims over the Web. In CLEF (working notes)

Haouari F, Hasanain M, Suwaileh R, Elsayed T (2021) ArCOV19-rumors: Arabic COVID-19 twitter dataset for misinformation detection. ArXiv201008768 Cs

Hardalov M, Arora A, Nakov P, Augenstein I (2021) A survey on stance detection for mis- and disinformation identification. ArXiv210300242 Cs

Harrag F, Djahli MK (2022) Arabic fake news detection: a fact checking based deep learning approach. ACM Trans Asian Low-Resour Lang Inf Process 21:1–34. https://doi.org/10.1145/3501401

Helwe C, Elbassuoni S, Al Zaatari A, El-Hajj W (2019) Assessing arabic weblog credibility via deep co-learning. In: Proceedings of the Fourth Arabic natural language processing workshop. Presented at the proceedings of the fourth Arabic natural language processing workshop. Association for Computational Linguistics, Florence. pp 130–136. https://doi.org/10.18653/v1/W19-4614

Himdi H, Weir G, Assiri F, Al-Barhamtoshy H (2022) Arabic fake news detection based on textual analysis. Arab J Sci Eng 47(8):10453–10469

Jahng MR, Lee H, Rochadiat A (2020) Public relations practitioners’ management of fake news: exploring key elements and acts of information authentication. Public Relat Rev 46:101907. https://doi.org/10.1016/j.pubrev.2020.101907

Jardaneh G, Abdelhaq H, Buzz M, Johnson D (2019) Classifying Arabic tweets based on credibility using content and user features. In: 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). Presented at the 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). IEEE, Amman. pp 596–601. https://doi.org/10.1109/JEEIT.2019.8717386

Jones-Jang SM, Mortensen T, Liu J (2021) Does media literacy help identification of fake news? Information literacy helps, but other literacies don’t. Am Behav Sci 65:371–388. https://doi.org/10.1177/0002764219869406

Karimi H, Roy P, Saba-Sadiya S, Tang J (2018) Multi-source multi-class fake news detection. In Proceedings of the 27th international conference on computational linguistics, pp 1546–1557

Kaur S, Kumar P, Kumaraguru P (2020) Automating fake news detection system using multi-level voting model. Soft Comput 24:9049–9069. https://doi.org/10.1007/s00500-019-04436-y

Khalil A, Jarrah M, Aldwairi M, Jaradat M (2022) AFND: Arabic fake news dataset for the detection and classification of articles credibility. Data Brief 42:108141

Khan JY, Khondaker MdTI, Afroz S, Uddin G, Iqbal A (2021) A benchmark study of machine learning models for online fake news detection. Mach Learn Appl 4:100032. https://doi.org/10.1016/j.mlwa.2021.100032

Khouja J (2020) Stance prediction and claim verification: an Arabic perspective. ArXiv200510410 Cs

Lewandowsky S, van der Linden S (2021) Countering misinformation and fake news through inoculation and prebunking. Eur Rev Soc Psychol:1–38. https://doi.org/10.1080/10463283.2021.1876983

Lim G, Perrault ST (2020) Perceptions of News sharing and fake news in Singapore. ArXiv201007607 Cs

Mahlous AR, Al-Laith A (2021) Fake news detection in arabic tweets during the COVID-19 pandemic. Int J Adv Comput Sci Appl 12. https://doi.org/10.14569/IJACSA.2021.0120691

Mohammad S, Kiritchenko S, Sobhani P, Zhu X, Cherry C (2016) SemEval-2016 task 6: detecting stance in tweets, proceedings of the 10th international workshop on Semantic Evaluation (SemEval-2016). Association for Computational Linguistics, San Diego, pp 31–41. https://doi.org/10.18653/v1/S16-1003

Nagoudi EMB, Elmadany A, Abdul-Mageed M, Alhindi T, Cavusoglu H (2020) Machine generation and detection of Arabic manipulated and fake news. arXiv preprint arXiv:2011.03092

Najadat H, Tawalbeh M, Awawdeh R (2022) Fake news detection for Arabic headlines-articles news data using deep learning. Int J Elec Comput Eng (2088–8708) 12(4):3951

Nakov P, Barrón-Cedeno A, Elsayed T, Suwaileh R, Màrquez L, Zaghouani W, Atanasova P, Kyuchukov S, Da San Martino G (2018) Overview of the CLEF-2018 CheckThat! Lab on automatic identification and verification of political claims. In Experimental IR meets multilinguality, multimodality, and interaction: 9th International Conference of the CLEF Association, CLEF 2018, Avignon, France, Proceedings 9. Springer International Publishing, pp 372–387

Nakov P, Alam F, Shaar S, Martino GDS, Zhang Y (2021) A second pandemic? Analysis of fake news about COVID-19 vaccines in Qatar. ArXiv210911372 Cs

Nassif AB, Elnagar A, Elgendy O, Afadar Y (2022) Arabic fake news detection based on deep contextualized embedding models. Neural Comput Appl 34(18):16019–16032

Niewinski P, Pszona M, Janicka M (2019) GEM: generative enhanced model for adversarial attacks. Proceedings of the second workshop on Fact Extraction and VERification (FEVER). Association for Computational Linguistics, Hong Kong, pp 20–26. https://doi.org/10.18653/v1/D19-6604

Noman Qasem S, Al-Sarem M, Saeed F (2022) An ensemble learning based approach for detecting and tracking COVID19 rumors. Comput Mater Contin 70:1721–1747. https://doi.org/10.32604/cmc.2022.018972

Oshikawa R, Qian J, Wang WY (2018) A survey on natural language processing for fake news detection. arXiv preprint arXiv:1811.00770

Rampersad G, Althiyabi T (2020) Fake news: acceptance by demographics and culture on social media. J Inf Technol Polit 17:1–11. https://doi.org/10.1080/19331681.2019.1686676

Rangel F, Giachanou A, Ghanem BHH, Rosso P (2020) Overview of the 8th author profiling task at pan 2020: profiling fake news spreaders on twitter. In CEUR workshop proceedings. Sun SITE Central Europe, (vol. 2696, pp 1–18)

Ruchansky N, Seo S, Liu Y (2017) CSI: a hybrid deep model for fake news detection. Proceedings of the 2017 ACM on conference on information and knowledge management. Singapore, pp 797–806. https://doi.org/10.1145/3132847.3132877

Saadany H, Mohamed E, Orasan C (2020) Fake or real? A study of Arabic satirical fake news. ArXiv201100452 Cs

Sabbeh SF, Baatwah SY (2018) Arabic news credibility on twitter: an enhanced model using hybrid featureS. J Theor Appl Inf Technol 96(8)

Saeed NM, Helal NA, Badr NL, Gharib TF (2020) An enhanced feature-based sentiment analysis approach. Wiley Interdiscip Rev: Data Min Knowl Disc 10(2):e1347

Google Scholar

Saeed RM, Rady S, Gharib TF (2021) Optimizing sentiment classification for Arabic opinion texts. Cogn Comput 13(1):164–178

Saeed NM, Helal NA, Badr NL, Gharib TF (2018) The impact of spam reviews on feature-based sentiment analysis. In 2018 13th Int Conf Comput Eng Sys (ICCES) IEEE, pp 633–639

Sahoo SR, Gupta BB (2021) Multiple features based approach for automatic fake news detection on social networks using deep learning. Appl Soft Comput 100:106983. https://doi.org/10.1016/j.asoc.2020.106983

Shahi GK, Struß JM, Mandl T (2021) Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection. Working Notes of CLEF

Shu K, Wang S, Liu H (2017) Exploiting tri-relationship for fake news detection. arXiv preprint arXiv:1712.07709, 8

Shu K, Mahudeswaran D, Wang S, Liu H (2020) Hierarchical propagation networks for fake news detection: Investigation and exploitation. In Proceedings of the international AAAI conference on web and social media (vol. 14, pp 626–637)

Sitaula N, Mohan CK, Grygiel J, Zhou X, Zafarani R (2020) Credibility-based fake news detection. In: Disinformation, Misinformation, and fake news in social media. Springer, Cham, pp 163–182

Thaher T, Saheb M, Turabieh H, Chantar H (2021) Intelligent detection of false information in arabic tweets utilizing hybrid harris hawks based feature selection and machine learning models. Symmetry 13:556. https://doi.org/10.3390/sym13040556

Touahri I, Mazroui A (2018) Opinion and sentiment polarity detection using supervised machine learning. In 2018 IEEE 5th Int Congr Inf Sci Technol (CiSt) IEEE, pp 249–253

Touahri I, Mazroui A (2019) Automatic verification of political claims based on morphological features. In CLEF (working notes)

Touahri I, Mazroui A (2020) Evolution team at CLEF2020-CheckThat! lab: integration of linguistic and sentimental features in a fake news detection approach. In CLEF (working notes)

Visentin M, Pizzi G, Pichierri M (2019) Fake news, real problems for brands: the impact of content truthfulness and source credibility on consumers’ behavioral intentions toward the advertised brands. J Interact Mark 45:99–112. https://doi.org/10.1016/j.intmar.2018.09.001

Wang Y, Yang W, Ma F, Xu J, Zhong B, Deng Q, Gao J (2020) Weak supervision for fake news detection via reinforcement learning. Proc AAAI Conf Artif Intell 34:516–523. https://doi.org/10.1609/aaai.v34i01.5389

Wang Y, Ma F, Jin Z, Yuan Y, Xun G, Jha K, Su L, Gao J (2018) Eann: event adversarial neural networks for multi-modal fake news detection. In Proceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining, pp 849–857

Wang J, Makowski S, Cieślik A, Lv H, Lv Z (2023) Fake news in virtual community, virtual society, and metaverse: a survey. IEEE Trans Comput Soc Sys

Wang WY (2017) "Liar, liar pants on fire": a new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648

Yafooz W, Emara AHM, Lahby M (2022) Detecting fake news on COVID-19 vaccine from YouTube videos using advanced machine learning approaches. In: Combating fake news with computational intelligence techniques. Springer, Cham, pp 421–435

Zhang J, Dong B, Yu PS (2020) FakeDetector: effective fake news detection with deep diffusive neural network. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE). Presented at the 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, Dallas, pp 1826–1829. https://doi.org/10.1109/ICDE48307.2020.00180

Zhang X, Ghorbani AA (2020) An overview of online fake news: characterization, detection, and discussion. Inf Process Manage 57(2):102025

Zhou X, Zafarani R (2020) A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput Surv (CSUR) 53(5):1–40

Zhou X, Jain A, Phoha VV, Zafarani R (2020) Fake news early detection: a theory-driven model. Digit Threats Res Pract 1:1–25. https://doi.org/10.1145/3377478

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ORIGINAL RESEARCH article

Introduction

Literature Review

Social Media Use, Social Capital, and Psychological Well-Being

Social Media Use, Social Isolation, and Psychological Well-Being

Social Media Use, Smartphone Addiction, Phubbing, and Psychological Well-Being

Indirect Relationship Between Social Media Use and Psychological Well-Being

Research Methodology

Data Collection Procedures and Respondent's Information

Measurement Items

Social Media Use

Social Capital

Social Isolation

Smartphone Addiction

Psychological Well-Being

Data Analysis

Common Method Bias (CMB) Test

Assessment of Measurement Model

Assessment of the Structural Model

Theoretical Contributions

Practical Contributions

Limitations and Directions for Future Studies

Data Availability Statement

Ethics Statement

Author Contributions

Conflict of Interest

Social Media Methods

Toward Improved Methods in Social Media Research

Introduction

Current Research Practices

Patterns in Research Practices

Pitfalls and Solutions in Current Research Practices

Specificity of Social Media Use Data

Ecological Validity

Table 1: Methodological Overview of Reviewed Studies

Social impact in social media: A new method to evaluate the social impact of research

Introduction

Literature review

Methods and data presentation

Data collection

Data analysis

Analytical categories and codebook

How to analyse Twitter and Facebook data

Quantitative evidence of social impact in social media

Qualitative evidence of social impact in social media

Interrater reliability (kappa)

Conclusions

Supporting information

S2 File. Data collected and SICOR calculation.

Acknowledgments

An Evidence-based Perspective on Social Media Use Among Young People

Evidence of ways social media may differentially support youth well-being

Evidence of ways social media may exacerbate harm among diverse subgroups of young people

Evidence of ways young people take actions to balance risks and benefits

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