social media for research dissemination

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PMC10221530

The Use of Social Media for Dissemination of Research Evidence to Health and Social Care Practitioners: Protocol for a Systematic Review

Sarah f roberts-lewis.

1 Population Health Research Institute, St George's University of London, London, United Kingdom

Helen A Baxter

2 Bristol Population Health Science Institute, University of Bristol, Bristol, United Kingdom

3 National Institute of Health and Care Research, London, United Kingdom

4 Faculty of Health, Social Care and Education, St George's University of London, London, United Kingdom

Sophia Quirke-McFarlane

5 School of Psychology, University of Surrey, Guildford, United Kingdom

Fiona J Leggat

Hannah m garner.

6 Department of Physiotherapy, St George’s University Hospitals NHS Foundation Trust, London, United Kingdom

Martha Powell

Sarah white, lindsay bearne, associated data.

Search strategy.

Data extraction form.

Risk of Bias tools.

The data sets generated and analyzed during this study will be available from the corresponding author on reasonable request. The full search strategy is available in Multimedia Appendix 1 .

Effective dissemination of research to health and social care practitioners enhances clinical practice and evidence-based care. Social media use has potential to facilitate dissemination to busy practitioners.

This is a protocol for a systematic review that will quantitatively synthesize evidence of the effectiveness of social media, compared with no social media, for dissemination of research evidence to health and social care practitioners. Social media platforms, formats, and sharing mechanisms used for effective dissemination of research evidence will also be identified and compared.

Electronic database searches (MEDLINE, PsycINFO, CINAHL, ERIC, LISTA, and OpenGrey) will be conducted from January 1, 2010, to January 10, 2023, for studies published in English. Randomized, nonrandomized, pre-post study designs or case studies evaluating the effect of social media on dissemination of research evidence to postregistration health and social care practitioners will be included. Studies that do not involve social media or dissemination or those that evaluate dissemination of nonresearch information (eg, multisource educational materials) to students or members of the public only, or without quantitative data on outcomes of interest, will be excluded. Screening will be carried out by 2 independent reviewers. Data extraction and quality assessment, using either the Cochrane tool for assessing risk of bias or the Newcastle-Ottawa Scale, will be completed by 2 independent reviewers. Outcomes of interest will be reported in 4 domains (reach, engagement, dissemination, and impact). Data synthesis will include quantitative comparisons using narrative text, tables, and figures. A meta-analysis of standardized pooled effects will be undertaken, and subgroup analyses will be applied, if appropriate.

Searches and screening will be completed by the end of May 2023. Data extraction and analyses will be completed by the end of July 2023, after which findings will be synthesized and reported by the end of October 2023.

Conclusions

This systematic review will summarize the evidence for the effectiveness of social media for the dissemination of research evidence to health and social care practitioners. The limitations of the evidence may include multiple outcomes or methodological heterogeneity that limit meta-analyses, potential risk of bias in included studies, and potential publication bias. The limitations of the study design may include potential insensitivity of the electronic database search strategy. The findings from this review will inform the dissemination practice of health and care research.

Trial Registration

PROSPERO CRD42022378793; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=378793

International Registered Report Identifier (IRRID)

DERR1-10.2196/45684

Introduction

Health and social care researchers aim for their research findings to be accessible and useful for practitioners to help them deliver best, evidence-based clinical care and improve patient outcomes [ 1 - 4 ]. Thus, as part of continuing professional development, practitioners need to access relevant high-quality research evidence [ 5 , 6 ]. Research evidence is defined as information provided by a research study; information may derive from original studies (primary research), reviews (secondary research), or evidence-based guidelines [ 6 ]. Effective strategies are required to deliver relevant research evidence to practitioners [ 2 , 7 , 8 ] because dissemination of research evidence without delay is recommended to maximize its benefits [ 9 , 10 ].

Dissemination is defined as active approaches that use specific channels and planned strategies to get research evidence to a specific audience (who can make use of it and enact research benefits) [ 10 ]. Challenges of dissemination to practitioners include organizational barriers, limited professional opportunities, time constraints, and accessing relevant articles within the exponentially increasing volume of research evidence produced each year [ 8 , 11 - 13 ]. Social media has potential to overcome some of the barriers to dissemination [ 7 , 8 ].

Social media is defined as a collection of web-based platforms that allow the creation and exchange of user-generated content [ 14 ]. Open social media are accessible to anyone; closed social media groups have eligibility parameters that limit user participation [ 15 ]. The number of people who use social media globally has risen from millions to billions in the last 20 years; its advantages include not being limited in time and space [ 7 , 16 ]. Thus, professional use of social media by health and social care practitioners represents an opportunity for effective dissemination of research evidence [ 15 ]. Indeed, social media is increasingly used to disseminate health and social care research [ 8 , 15 , 17 - 21 ].

Social media use in health care has been studied and reviewed extensively [ 22 - 28 ]. Reviews concur about benefits and risks of social media for reputation, communication, information sharing, and public health messages. For health care professionals, social media has also been used effectively for education and day-to-day communications [ 25 , 29 , 30 ].

The existing reviews of social media for dissemination of health care research have narratively synthesized benefits and risks, described similarities, differences, and qualitative experiences, or provided commentaries on the mechanisms and potential uses of social media for dissemination [ 15 , 25 , 26 , 31 ]. However, they are heterogeneous in terms of study design and perspectives [ 15 , 26 , 31 ]. In 2018, a review of reviews about effective uses of social media in public health and medicine included little evidence concerning dissemination [ 25 ]. In 2020, a review highlighted 4 research dissemination case studies [ 26 ]. An unpublished preprint review, which identified 4 randomized controlled trials and 37 quantitative or descriptive studies, identified Twitter as a prominent platform for dissemination [ 31 ]. A subsequent review in 2022 highlighted the communication mechanisms and potential of social media for both 1-way knowledge mobilization (ie, dissemination) and 2-way knowledge mobilization (ie, more complex multidirectional integrated knowledge translation involving collaborative interactions between researchers and practitioners) [ 15 ]. The existing reviews are health care focused; no reviews have investigated dissemination to social care practitioners.

Although quantitative reports and comparisons of social media for dissemination of research evidence in health care are emerging [ 18 - 20 , 32 ], it is not yet clear how consistent or robust their findings are. To date, no quantitative synthesis or meta-analysis has been undertaken to investigate the effectiveness of open social media for dissemination of research evidence to health and social care practitioners.

The objective of this systematic review is to quantitatively synthesize evidence of the effectiveness of social media, compared to no social media, for dissemination of research evidence to health and social care practitioners. The social media platforms, formats, and sharing mechanisms used for effective dissemination of research evidence will also be identified and compared.

The specific research questions are as follows: (1) how effective is open social media for dissemination of research evidence for health and social care practitioners, compared to no social media input? (2) Which social media platforms, formats, and sharing mechanisms are used for dissemination of research evidence for health and social care practitioners? (3) What is the comparative effectiveness for dissemination of research evidence to health and social care practitioners between different social media platforms (eg, Facebook vs Twitter), different formats of social media posts (eg, text vs infographic vs video), and different social media–sharing mechanisms (eg, site-wide shares vs special interest groups vs live social media events vs influencer endorsements)

The inception and design of this systematic review, of articles available from January 1, 2010, to January 10, 2023, incorporated patient and public involvement via consultations with a stakeholder group. This protocol follows PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [ 33 ]. The protocol has been registered with the International Prospective Register of Systematic Reviews (PROSPERO; registration number: CRD42022378793).

Eligibility

Table 1 summarizes the population, intervention, comparisons, and outcomes of interest. The target population will be practitioners, defined as health and social care professionals, including, but not limited to, some of the largest groups of registered practitioners in the United Kingdom; that is, nurses, doctors, social workers, midwives, pharmacists, physiotherapists, occupational therapists, radiographers, and paramedics [ 34 - 38 ]. We will include both individual professions and collective groups of practitioners.

Definitions of participants, intervention, comparisons, and outcomes. The numbers in square brackets denote prioritization for analyses if more than one outcome is reported in a single study, according to the most reported outcomes. Prioritization will ensure outcomes are only represented once per study in quantitative synthesis and meta-analysis.

The intervention will be open social media used for dissemination of research evidence. We define research evidence as information from peer-reviewed articles featuring empirical findings that have met the publication standards of their specialty. Evidence may take the form of an original research article, or a group of original research articles identified and synthesized systematically. In this review, only research evidence that is professionally relevant to health and social care practitioners will be considered; however, our definition includes open-access research evidence (and social media posts where practitioners are the target audience), even when other audiences, like the general public, also have access.

We define open social media as social networking and media-sharing sites or platforms that allow any user to make a one-to-many post and to interact by responding to posts. Our definition of social media differs from broader existing definitions of social media, such as that of Kaplan and Haelein [ 14 ], who consider social media as web-based applications that allow the creation and exchange of user-generated content. Our more focused definition was used to target social media with the greatest potential for knowledge mobilization between the different communities of research and clinical settings. The current literature in knowledge mobilization has highlighted the importance of open, 2-way interaction to create linkage between communities and facilitate knowledge sharing. In this context, dissemination of research is an active, relational, multidirectional, and collaborative process that interacts with the standpoint of those who access, share, discuss, and use it [ 39 , 40 ]. Thus, we do not include mass media press articles, wikis, and blogs with no, or limited, facility for user interactions, purely communication-based applications or closed, fee-paying, or invite-only social media groups. However, these will be considered if they are highlighted using open social networking or media sharing sites, used as part of onward information sharing, or are groups that can be freely joined by any interested user (eg, by following a link available on open social networking and media-sharing sites).

We define social media–sharing mechanisms as the ways in which research evidence might be featured or interacted with, including, but not limited to, open sharing to the entire forum, live social media events, influencer endorsement, and accessible special interest groups that can be joined or followed. We define social media formats as including a variety of media types, including, but not limited to, text, illustrative pictures, visual abstracts, infographics, videos, and podcasts.

The study inclusion and exclusion criteria are summarized in Textbox 1 . Full-text, English-language studies will be included if they make quantitative comparison between dissemination of research evidence for health and social care practitioners using social media versus a controlled “no social media” condition (between group). Studies will also be included that quantify changes from baseline conditions to follow-up after an open social media campaign for dissemination of research evidence to health and social care practitioner (within group or case series data). Studies quantitatively comparing dissemination of research evidence for health and social care practitioners between different social media platforms, sharing mechanisms, and formats will also be included. Studies comparing topics of social media posts or research evidence will be excluded.

Summary of inclusion and exclusion criteria.

Inclusion criteria:

Peer-reviewed journal articles, study types including randomized controlled trials, case-controlled comparisons nonrandomized comparisons, pre-post designs, cohort studies, and case reports.
Articles published after 2010.
English-language articles or translations in English available.
Articles that quantitatively evaluate and compare open group social media in terms of reach, engagement, dissemination, and impact of research evidence for postregistration health and social care practitioners.

Exclusion criteria:

Study types including protocols, reviews, opinion pieces, and conference abstracts.
Articles published before 2010.
Articles not available in English.
Study populations not including health and social care practitioners (eg, no mention of practitioners, only focused on students, service users, patients, or the general public).
Social media interventions used only for sharing non health and social care–related research topics or other sorts of information (eg, multisource clinical education delivery, day-to-day interpersonal communications, and organizational or administrative information), closed, private, or invite-only groups, professional identity and reputation purposes, recruitment, or posts without a social networking component (eg, a blog or press article without signposting on social media networking sites).
Comparisons between information topics only (without other comparisons, eg, between social media and control conditions, or between social media sites, or types of media).
Articles that do not provide sufficient quantitative data on outcomes of interest or those reporting only qualitative data.

Information Sources

Searches will be carried out from January 1, 2010, to January 10, 2023. Preliminary searches revealed that there was unlikely to be relevant literature prior to 2010; whereas after 2010, reports of social media use in research dissemination increased. Searches will be carried out in electronic bibliographic databases including MEDLINE (Ovid), PsycINFO (Ovid), CINAHL plus (EBSCO), ERIC (EBSCO), LISTA, and OpenGrey. Search engines, including PubMed, elicit, and Google Scholar, will also be used for citation searches and reference harvesting. Bibliographic hand-searching of relevant systematic reviews and included articles will also be carried out. The search strategy is likely to be adequate to ameliorate selection and detection biases.

Search Strategy

The search strategies were informed by previous review searches [ 15 , 31 , 41 ] (see the search terms in Table 2 and the example search strategies in Multimedia Appendix 1 ).

Summary of key search terms.

Data Management

The search results will be exported to, and deduplicated in, Endnote online (Clarivate), then imported into Rayyan software (Rayyan Systems) for title, abstract, and full-text screening [ 42 ].

Selection of Studies

Two reviewers (SRL and SQM) will independently screen titles and abstracts for eligibility. Potentially eligible studies will be obtained in full text and the eligibility criteria applied. Two of 5 independent reviewers (from a team including SRL, SQM, LB, HG, and FL) will screen all full-text articles against the eligibility criteria. If no consensus can be reached between reviewers, eligibility will be settled by a third reviewer (HB). Articles will be included only if they provide sufficient quantitative data to allow extrapolation of mean (SD) outcome data per social media post for 2 comparable groups. Reasons for article exclusion will be recorded according to the PRISMA recommendations [ 43 ].

Data Extraction

The data will be extracted from included full-text articles by 2 of 5 reviewers (SRL and SQM/LB/HG/FL) independently using a data extraction form developed a priori and adapted from the previous relevant reviews [ 26 , 31 ] (see Multimedia Appendix 2 ). Consensus on extracted data accuracy will be achieved by discussion and settled by a third reviewer (HB) in the case of disagreement. Corresponding authors of eligible studies will be contacted to request additional data when required.

Outcome Data

The quantitative outcomes will be grouped into 4 domains (reach, engagement, dissemination, and impact; Table 1 ) [ 27 , 44 ]. Outcome measures will be grouped by domain because a heterogeneous range of outcomes and study designs are characteristic of the social media literature [ 15 , 25 ]. There is no primary outcome because dissemination of research evidence via social media is a complex multidimensional behavior that cannot be distilled into a single outcome [ 25 , 27 ]. Different outcomes measuring the same concept will be combined for meta-analysis wherever appropriate. Where multiple outcomes reporting the same concept are reported in a single study, hierarchical outcome prioritization will be used, preferentially selecting the most frequently reported variables (see Table 1 ).

The difference between group means for each comparison group will be obtained, or the mean difference from baseline to follow-up in pre-post designs. To accompany means, SD and 95% CI will be extracted. Wherever possible missing means and SDs will be calculated from other reported statistics (eg, median and IQR, as per Cochrane Handbook instructions Section 5.6 and Chapter 6) [ 45 ]. For each outcome, data will be simplified by dividing by the number of social media posts, to yield comparable quantitative data per research evidence–related social media post.

Risk of Bias and Quality Rating of Evidence

For randomized controlled trials, the Cochrane tool for assessing risk of bias (ROB-2) will be used [ 46 ]. For nonrandomized controlled trials, the Newcastle-Ottawa Scale (NOS) [ 47 ] will be used to assess risk of bias, as per 2019 Cochrane recommendations [ 45 ]. Both the ROB-2 and the NOS will be adapted to suit the types of studies selected in this systematic review because studies testing social media dissemination are unlikely to use typical participant grouping designs. The NOS was selected in preference to the latest Cochrane tool for assessing risk of bias in nonrandomized studies (ROBINS; consisting of ROBINS-I for intervention studies and ROBINS-E for exposure studies [ 48 , 49 ]). This was because the complexity of these tools will not allow adaptation to alternative study designs and 1 tool (ROBINS-E for exposure studies) is still in development.

ROB-2 consists of 34 items, divided into 6 scoring domains that differentiate between lower risk of bias and higher risk of bias [ 46 ]. The maximum rating of the NOS is 9, a score of 0-3 is considered high risk of bias, 4-6 considered a medium risk of bias, and 7-9 considered a low risk of bias ( Multimedia Appendix 3 ) [ 47 ]. Risk of bias will be included in results tables and figures that describe each included study.

Data Synthesis

Results will be summarized narratively with text, tables, and figures. Quantitative comparisons between studies and groups will include mean, SD, and simplified group means weighted per research evidence–related social media post.

Between, and within for pre-post designs, group differences, SDs, and standardized effect sizes (eg, Cohen d or Hedges g ) will be used to compare effects between included studies. Effect sizes of ≥0.8 will be defined as large, ≥0.5 as moderate, and ≥0.2 as small [ 50 ].

For outcomes that are available in a sufficient number of studies, pooled effects will be tested in meta-analyses using RevMan (version 5.3; The Cochrane Collaboration). Heterogeneity will be tested using I 2 . To test pooled effects, a fixed-effects model will be used if I 2 is less than 50% or a random-effects model will be used if I 2 is greater than 50%. If a comparison has I 2 >75% it will be removed from meta-analysis and synthesized narratively instead. Subgroup analyses will be used for heterogeneous study groups. Publication bias will be assessed using funnel plots.

Confidence in Cumulative Evidence

For each outcome, the uncertainty of the evidence base will be evaluated based on the grading of recommendations assessment, development, and evaluation approach [ 51 ]. This includes the following 5 domains: study limitations, imprecision, indirectness, inconsistency, and publication bias. The grading of recommendations assessment, development, and evaluation domains will be used to upgrade or downgrade evidence after initial assessment. Quality of evidence will be categorized as high, moderate, low, or very low [ 52 , 53 ].

Searches and screening will be completed by the end of May 2023 and recorded in a PRISMA flowchart ( Figure 1 ) [ 43 ].

An external file that holds a picture, illustration, etc.
Object name is resprot_v12i1e45684_fig1.jpg

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart.

Data extraction and analyses will be completed by the end of July 2023, after which findings will be synthesized.

We expect that this study will be completed by the end of October 2023. This review will synthesize evidence of the effectiveness of open social media for dissemination of research evidence to health and social care practitioners. We anticipate that we will find evidence that social media is effective, although there may be variation between its effect on the 4 outcome domains (reach, engagement, direct dissemination, and impact).

The previous reviews have highlighted the potential of social media for a variety of purposes in health care [ 15 , 25 , 26 , 31 , 54 ], although dissemination of research evidence currently accounts for only 1% of health research concerning social media [ 55 ]. Two existing reviews suggest that social media might be effective to improve direct dissemination and impact of research articles [ 31 ] and reach and engagement with clinical guidelines [ 54 ]; however, quantitative syntheses and meta-analyses were not performed.

Qualitative reviews have highlighted strategies that may enhance the dissemination of research evidence on social media [ 15 , 26 , 31 , 54 ]. These include enlisting people with expertise in social media at the planning stage, using professional social media marketing services and involving target users in content design. Use of multiple social media platforms is implicated, although Twitter is most often highlighted for professional use. Social media formats include using media that are accessible, relevant, useful, authentic, credible, and visually appealing to target users, a range of multimedia formats (including good quality images and infographics, text, videos, and podcasts, with images highlighted as being particularly important for social media reach and engagement). Social media–sharing mechanisms include involving key influencers and networks with the most connectedness in the field (such as organizational newsletters, journals, or individuals) to enhance social filtering, creating or enlisting existing web-based communities of practice, identifying and using key hashtags, posting at planned times (potentially including weekdays, weekday evenings, and weekends), posting regularly for a sustained campaign, and coordinating engagement events (such as journal clubs, question and answer sessions, or live interviews).

This systematic review will be the first to use quantitative, meta-analytical synthesis of evidence for the effectiveness of social media for research dissemination to practitioners. The limitations of the evidence may include multiple outcomes or methodological heterogeneity that limit meta-analyses, potential risk of bias in included studies, and potential publication bias. The limitations of the study design may include potential insensitivity of the electronic database search strategy to specific professional disciplines or social media platforms; however, bibliographic citation searching of included articles and relevant systematic reviews is likely to compensate for this. As social media are rapidly evolving, this systematic review will need to be repeated regularly to consider the impact of new and favored platforms and the search strategy revised to account for this.

The conclusions of this systematic review will inform the use of social media for the dissemination of research evidence to health and social care practitioners. This information will be important for researchers, research funders, and governmental bodies who have a remit to share their research evidence to inform practice and care. The findings of this review could be complemented by investigation of different practitioners’ attitudes and experiences of professional social media use so that targeted dissemination strategies could be developed. Future research directions include quantitative synthesis and meta-analysis of evidence for the effectiveness of closed social media groups for the dissemination of research evidence to health and social care practitioners. The interaction between closed and open social media may enhance knowledge sharing and engagement [ 15 ].

Dissemination Plan

The findings from this systematic review will be disseminated through a peer-reviewed journal article, presentations at academic conferences and promoted using social media.

Acknowledgments

This study presents independent research funded by National Institute of Health and Care Research (NIHR) UK (NIHR evidence [2022/01]). The views expressed are those of the authors and not necessarily those of the National Health Service, the NIHR, or the Department of Health.

Abbreviations

Multimedia appendix 1, multimedia appendix 2, multimedia appendix 3, data availability.

Conflicts of Interest: None declared.

This paper is in the following e-collection/theme issue:

Published on 12.5.2023 in Vol 12 (2023)

The Use of Social Media for Dissemination of Research Evidence to Health and Social Care Practitioners: Protocol for a Systematic Review

Authors of this article:

Sarah F Roberts-Lewis 1 , BSc ;
Helen A Baxter 2, 3 , PhD ;
Gill Mein 4 , BSc ;
Sophia Quirke-McFarlane 5 , MSc ;
Fiona J Leggat 1 , PhD ;
Hannah M Garner 6 , BSc ;
Martha Powell 3 , BSc ;
Sarah White 1 , PhD ;
Lindsay Bearne 1, 3 , PhD

1 Population Health Research Institute, St George's University of London, London, United Kingdom

2 Bristol Population Health Science Institute, University of Bristol, Bristol, United Kingdom

3 National Institute of Health and Care Research, London, United Kingdom

4 Faculty of Health, Social Care and Education, St George's University of London, London, United Kingdom

5 School of Psychology, University of Surrey, Guildford, United Kingdom

6 Department of Physiotherapy, St George’s University Hospitals NHS Foundation Trust, London, United Kingdom

Corresponding Author:

Sarah F Roberts-Lewis, BSc

Population Health Research Institute

St George's University of London

Cranmer Terrace

London, SW17 0RE

United Kingdom

Phone: 44 020 8725 0368

Email: [email protected]

Background: Effective dissemination of research to health and social care practitioners enhances clinical practice and evidence-based care. Social media use has potential to facilitate dissemination to busy practitioners.

Objective: This is a protocol for a systematic review that will quantitatively synthesize evidence of the effectiveness of social media, compared with no social media, for dissemination of research evidence to health and social care practitioners. Social media platforms, formats, and sharing mechanisms used for effective dissemination of research evidence will also be identified and compared.

Methods: Electronic database searches (MEDLINE, PsycINFO, CINAHL, ERIC, LISTA, and OpenGrey) will be conducted from January 1, 2010, to January 10, 2023, for studies published in English. Randomized, nonrandomized, pre-post study designs or case studies evaluating the effect of social media on dissemination of research evidence to postregistration health and social care practitioners will be included. Studies that do not involve social media or dissemination or those that evaluate dissemination of nonresearch information (eg, multisource educational materials) to students or members of the public only, or without quantitative data on outcomes of interest, will be excluded. Screening will be carried out by 2 independent reviewers. Data extraction and quality assessment, using either the Cochrane tool for assessing risk of bias or the Newcastle-Ottawa Scale, will be completed by 2 independent reviewers. Outcomes of interest will be reported in 4 domains (reach, engagement, dissemination, and impact). Data synthesis will include quantitative comparisons using narrative text, tables, and figures. A meta-analysis of standardized pooled effects will be undertaken, and subgroup analyses will be applied, if appropriate.

Results: Searches and screening will be completed by the end of May 2023. Data extraction and analyses will be completed by the end of July 2023, after which findings will be synthesized and reported by the end of October 2023.

Conclusions: This systematic review will summarize the evidence for the effectiveness of social media for the dissemination of research evidence to health and social care practitioners. The limitations of the evidence may include multiple outcomes or methodological heterogeneity that limit meta-analyses, potential risk of bias in included studies, and potential publication bias. The limitations of the study design may include potential insensitivity of the electronic database search strategy. The findings from this review will inform the dissemination practice of health and care research.

Trial Registration: PROSPERO CRD42022378793; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=378793

International Registered Report Identifier (IRRID): DERR1-10.2196/45684

Introduction

Eligibility

Summary of inclusion and exclusion criteria.

Inclusion criteria:

Peer-reviewed journal articles, study types including randomized controlled trials, case-controlled comparisons nonrandomized comparisons, pre-post designs, cohort studies, and case reports.
Articles published after 2010.
English-language articles or translations in English available.
Articles that quantitatively evaluate and compare open group social media in terms of reach, engagement, dissemination, and impact of research evidence for postregistration health and social care practitioners.

Exclusion criteria:

Study types including protocols, reviews, opinion pieces, and conference abstracts.
Articles published before 2010.
Articles not available in English.
Study populations not including health and social care practitioners (eg, no mention of practitioners, only focused on students, service users, patients, or the general public).
Social media interventions used only for sharing non health and social care–related research topics or other sorts of information (eg, multisource clinical education delivery, day-to-day interpersonal communications, and organizational or administrative information), closed, private, or invite-only groups, professional identity and reputation purposes, recruitment, or posts without a social networking component (eg, a blog or press article without signposting on social media networking sites).
Comparisons between information topics only (without other comparisons, eg, between social media and control conditions, or between social media sites, or types of media).
Articles that do not provide sufficient quantitative data on outcomes of interest or those reporting only qualitative data.

Information Sources

Search Strategy

The search strategies were informed by previous review searches [ 15 , 31 , 41 ] (see the search terms in Table 2 and the example search strategies in Multimedia Appendix 1 ).

Data Management

The search results will be exported to, and deduplicated in, Endnote online (Clarivate), then imported into Rayyan software (Rayyan Systems) for title, abstract, and full-text screening [ 42 ].

Selection of Studies

Data Extraction

Outcome Data

Risk of Bias and Quality Rating of Evidence

Data Synthesis

Confidence in Cumulative Evidence

Searches and screening will be completed by the end of May 2023 and recorded in a PRISMA flowchart ( Figure 1 ) [ 43 ].

Data extraction and analyses will be completed by the end of July 2023, after which findings will be synthesized.

Dissemination Plan

The findings from this systematic review will be disseminated through a peer-reviewed journal article, presentations at academic conferences and promoted using social media.

Acknowledgments

Data Availability

The data sets generated and analyzed during this study will be available from the corresponding author on reasonable request. The full search strategy is available in Multimedia Appendix 1 .

Conflicts of Interest

None declared.

Search strategy.

Data extraction form.

Risk of Bias tools.

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Abbreviations

Edited by T Leung; submitted 12.01.23; peer-reviewed by M Brunner, A Hasen; comments to author 19.02.23; revised version received 27.02.23; accepted 27.02.23; published 12.05.23

©Sarah F Roberts-Lewis, Helen A Baxter, Gill Mein, Sophia Quirke-McFarlane, Fiona J Leggat, Hannah M Garner, Martha Powell, Sarah White, Lindsay Bearne. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 12.05.2023.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.

LifeScienceToday

Research dissemination: how social media can be a useful tool.

With the ever increasing popularity of social media use, Demba Kandeh takes a look at why researchers and scientists should join the social media endeavor.

Demba Kandeh 11 Jul 2016

Social beings on social media

The emergence of social media communications can enhance research dissemination opportunities within and beyond the scientific community. If the philosophers’ argument that human beings are social beings is true, then social media can be seen as a respond to the human urge for socializing. Since its dawn, the Internet has always been a critical tool in human interactivity and given the current trends in social media that interactivity is thus increasing.

Traditionally, when you interacted (offline) with family and friends as a researcher or scientist, once in a while you might find yourself discussing your work. Today, on the Internet and through social media you can interact with many more people and certainly, you can tell them about your exciting research work.

It is not an understatement to say that social media on the one hand is revolutionizing the communications industry. Social media channels, not just Facebook and Twitter (aka the obvious suspects) are increasingly gaining popularity among the public and more often are playing an integral role as communication tools. But there are many researchers and scientists who still shy away from social media, particularly Facebook and Twitter.

These days, social media waits for no one. If you’re LATE for the party, you’ll probably be covered by all the noise and you might not be able to get your voice across. It could only mean that if you want to be heard by the crowd, you have to be fast; and on social media, that means you have to be REALLY fast.” Aaron Lee, a top social media influencer .

I have met quite a number of researchers and once I hear about their research work I will rush to Twitter to find and follow them. More often than not, I am disappointed to know that they are not on Twitter. As a journalist, I have participated in live tweeting at various conferences/seminars and whereas most activists and other professionals can be easily found on social media, most researchers barely have a private Facebook account. But what is more striking is that those on social media, especially Twitter receive a lot more attention and engagement (primarily because they have audience both online and offline). So why should many researchers and scientists still focus on traditional means of research dissemination?

You have more chances to be heard

But with the increasing prevalence of social media, researchers and scientists should be fast adapting to using this not-so-new anymore new media tools. From Facebook to Twitter , WordPress to Youtube , among others, more and more researchers should opt to use social media channels for research dissemination. Of all the aspects of research impact, it is evident that visibility is very essential. A research by World Bank economists on the impact of economics blogs on research papers on economics found a significant correlation between blogging and increased in downloads.

It is common sense that people can only read your research if they know about it (if it is visible to them). And it has been established beyond any reasonable doubt that wide sharing of academic articles, especially through social media increases their visibility (and eventually downloads). Social media, more than any other tools offers the greatest chances for visibility if properly utilized. Twitter has over a quarter billion (about 310 million) active monthly users while Facebook reportedly has over 1.5 billion active monthly users. You cannot find anywhere else a larger population spanning peoples from across the globe with varied backgrounds and interests.

Some argue that social media is full of trolls and thus a “no go” area for serious business such as scientific research. The premise of this maybe true but the fact that social media is “full of trolls” is not a good enough reason to avoid it. On the contrary, social media is actually what you make it. The fact remains that there are trolls even in our scientific publishing world but as serious scientists we either ignore the trolls or as even better scientists we turn the trolls into serious conversations. Thus in conclusion, if you haven’t joined the conversation on social media yet, this should be a wake-up call. And if you’re already here, look out for how to improve on what you are doing.

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Should researchers blog? Arguments for a science blog - 28th July 2016
Research Dissemination: How social media can be a useful tool - 11th July 2016

Social media for research discourse, dissemination, and collaboration in rheumatology

Social media has become an important venue for rheumatologists, patients, organizations, and other stakeholders to discuss recent research advances in diagnosis and management of rheumatic disorders. In this article, we describe the current state of how social media may enhance dissemination, discourse, and collaboration in rheumatology research. Social media may refer to social platforms like Twitter and Instagram or digital media like podcasts and other websites that are operated for providing as free, open-access medical education (FOAM). Twitter has been one of the most active social media venues and continues to host a vibrant rheumatology community. Examples of research discussions on Twitter include organic user tweets, educational threads (“tweetorials”), live-tweeting academic conferences, and journals posting recently-accepted articles. Some research collaborations have been initiated through social media interactions. Social media may also directly contribute to research by facilitating the recruitment of study participants and the collection of survey-based data. Thus, social media is an evolving and important tool to enhance research discourse, dissemination, and collaboration in rheumatology.

Introduction

Social media has become integrated in all parts of life related to family, friends, work, school, news, and leisure. Like other medical specialties, [ 1 , 2 ] rheumatology has also found a place as a subject for discussion and knowledge-sharing on social media, and is used to connect people sharing rheumatology as a common sphere of interest, for disseminating or exchanging information about this topic. [ 3 ] Users may include people with rheumatic diseases, rheumatology providers, rheumatology clinical practices, academic journals, and rheumatology organizations. In this article, we will focus on the current landscape related to social media and rheumatology research. Social media can enhance the entire life cycle of research, including forming collaborations, collecting data, disseminating findings, and discussing impact and rigor. Twitter is currently the most active social media outlet for rheumatology.

However, we will also describe other social media platforms, including podcasts, blogs, Facebook, Instagram, TikTok, and other contributions to free, open-access, medical education (FOAM) related to rheumatology research.

Organic Tweets about Rheumatology Research

Organic tweets about rheumatology can lead to wide-reaching user engagement and provide an informal metric on enthusiasm for research findings or even identify urgent research needs. For example, a user may tweet about a recent article they found interesting or relevant to their practice. This tweet ends up on others’ feeds, particularly as others interact with and comment on it. This interaction can also be rooted in clinical scenarios. One should be very mindful that tweets live in the public domain, and so no identifying patient information, including dates or laboratory values, should be posted. Rheumatologists may also post about topics they are interested in, such as social determinants of health in rheumatic diseases or the importance of vaccination. Thus, organic tweets provide an ever-changing landscape of interest and discourse that helps to promote and disseminate research in rheumatology and identify research gaps in diagnosis, management, and treatment.

Tweetorials about Rheumatology Publications

While individual tweets are, by definition, ≤ 280 characters long (perhaps with images/videos), a user can post a string of individual tweets, referred to as a thread. Alternatively, some refer to this as a “tweetorial” when the content is educational, as used before in medical education. [ 4 , 5 ] Tweetorials are now a common way to disseminate findings about research papers, typically from authors of papers.

While each user has their own style for tweetorials, the series of tweets often mirrors the structure of a research paper ( Figure 1 ). Some tweetorials may be relatively brief (even as short as 2 tweets) or relatively lengthy. The first tweet often announces the paper’s recent acceptance into a journal with a link to the abstract or full paper at the journal’s website. Linking to the paper’s page on the journal website may enhance the Altmetrics for the publications, since most journals now track tweets as well as traffic to the abstract and full text on the website. Thus, tweets and tweetorials provide an important and timely method for journals to direct users directly to their website. The next series of tweets may be related to the background and research gap that the paper is attempting to fill. Some tweetorials may briefly summarize the methods used, particularly to highlight timely or novel strengths of the paper related to sample size, measurement, or statistical analysis. The next section of the tweetorial summarizes research findings. Finally, clinical implications, limitations, and future directions may be included in the tweetorial. Co-authors, institutions, and other researchers involved in or interested in the topic may be tagged in the tweetorial to alert them and garner more interest. User engagement is often enhanced by including the key figures from the paper and tagging other users interested in the research topic. An example of a tweetorial summarizing a recent research publication in rheumatology is mentioned in Figure 1 (or at https://twitter.com/zach_wallace_md/status/1569832766535073793).An example of a longer tweetorial is available here: https://twitter.com/jeffsparks/status/1570175303200411651

Figure 1 Example of tweetorial to disseminate research findings of a recently-published rheumatology paper. COVID-19/COVID19, coronavirus disease 2019; RA, rheumatoid arthritis; ILD, interstitial lung disease.

Example of tweetorial to disseminate research findings of a recently-published rheumatology paper. COVID-19/COVID19, coronavirus disease 2019; RA, rheumatoid arthritis; ILD, interstitial lung disease.

Live-Tweeting Rheumatology Conferences

Live-tweeting is roughly defined as tweeting about presentations in real time, occurring in-person or virtually. Live-tweeting has occurred sporadically for some time. However, the pandemic enhanced the number and reach of live-tweets. Prior to the pandemic, taking pictures of abstracts was generally discouraged, much less tweeting them to a wide audience. This sentiment has undergone a dramatic turn as conference organizers and researchers realized the benefits of research dissemination over social media. The risks of disseminating research findings prior to peer review have also been tempered with the wide acceptance of preprint journals as well as conference abstracts that are routinely published online. Similarly, images of posters at conferences are routinely tweeted and available for screenshotting on virtual platforms. However, many conferences do provide presenters the option to state publicly whether screenshots of images are allowed. With annual conferences going completely virtual in 2020, the American College of Rheumatology (ACR) introduced a volunteer Twitter Ambassador program that further encouraged live-tweeting.

The promotion of conference hashtags and live-tweeting is not new within rheumatology [ 6 ] or in medical and scientific conferences as a whole. [ 7 ] Even prior to the pandemic, the Twitter engagement increased each year and multiple fields in medicine have analyzed these data. [ 7 ] With more people introduced to the concept of live-tweeting rheumatology conferences each year, benefits and best practices should be considered ( Figure 2 ).

Anatomy of a medical conference tweet.

Benefits of Live-Tweeting Rheumatology Conferences

Active engagement. In trying to distill down information to fit in a post, you are actively engaging with and digesting the content, while encouraging discussion.

Promoting your own work and that of colleagues. Through social media, you may reach a wider audience than if you were presenting a poster at the in-person meeting.

Extending the conversation outside of the in-person conference. [ 8 , 9 ] Allowing people who are not able to attend the conference in-person to join in scientific discussions extends the meeting’s accessibility.

Best Practices for Live-Tweeting Rheumatology Conferences

Use the official conference hashtag.

Confirm permission to post photos. There are concerns that preliminary and unpublished data should not be widely shared online. When taking photos of speakers at their posters, it is preferred to ask the presenter if online posting is allowed.

Give proper attribution. Ensure there is a clear way to give attribution, at least by name, to the person who originally presented the work. Similar to other written work, avoid plagiarizing others’ tweets that may have been particularly impactful.

Keep threads short and intersperse the text with images.

Differentiate your content, opinions, and interpretations from those of the speakers. [ 8 ]

Tweeting in real time may lead to miscommunication. [ 7 ] It can be difficult to synthesize and distil down a talk in real time and write posts about it simultaneously – so consider limiting the number of posts or composing them later.

Social Media and Rheumatology Journals

Since the implementation and increasing availability of electronic journals, reading and uptake of the scientific literature have changed. The use of social media platforms, especially Twitter, has become an important tool both for medical learning and information dissemination, and rheumatology is no exception. [ 10 ]

Citation indices have been historically used to assess the impact of both scientific articles as well as academic journals. However, these metrics have been often criticized, and with the advent of social media, new alternative metrics (e.g., Altmetric score) that capture web-driven scholarly interactions have emerged. [ 11 ] These interactions include a variety of sources such as policy documents, news, blogs, and social media activity on platforms like Twitter, Facebook, and YouTube. Although there has been some debate about the discrepancy between social media presence and academic significance, recent studies have shown that Twitter promotion of manuscripts can have a positive impact on dissemination. [ 12 ] In a recent randomized study of cardiovascular articles, Twitter promotion was associated with both an increased Altmetric score as well as an increased number of citations. [ 13 ]

Use of social media for the identification of new literature is also appealing to readers. A survey highlighted the frequent use of social media platforms to follow journals as well as the high acceptance of article promotion through visual abstracts. [ 14 ] Given the opportunities provided by social media, rheumatology journals have increased their social media presence through the creation of Twitter accounts ( Table 1 ). [ 15 ] However, the use of Twitter for the promotion of recently-accepted and published articles still seems to be very variable, even if a Twitter account is active. Journals that actively promote articles through tweets may collect Twitter handle information at the time of acceptance to tag authors and institutions, and some even ask for a “tweet draft” to be associated with the publication link. Many rheumatology journals will tweet about articles soon after their acceptance. For researchers interested in the latest rheumatology findings, following journals on Twitter can be an optimal strategy to remain apprised of the most current literature.

Rheumatology journals and their Twitter activity as of November 2022

*Advisory boards composed by multiple individuals.

**Affiliated journals.

SoMe, social media; NA, not applicable.

Finally, journals have also incorporated the position of social media editors (also called digital editors or social media advisors) within their editorial boards ( Table 1 ). The role of social media editors is focused on the creation and dissemination of content in various forms compatible with effective online sharing, including graphical/visual abstracts, video interviews, and podcasts, to help build the journal’s brand. [ 16 ] The incorporation of social media editors/boards in rheumatology journals seems to have increased in the past few years, demonstrating the recognition of social media as an important tool in academic publishing. [ 17 ] Among 26 rheumatology journal families, 21 (81%) have active Twitter accounts. Nearly all high-impact rheumatology journals regularly tweet about recently accepted or published articles. Many also tweet tables of contents for issues. However, only 5 (19%) have a designated social media editor. Some journals also post about articles on other social media platforms such as Facebook and Instagram. Many journals and other rheumatology organizations now embed direct links to their respective social media accounts on the organization’s website. Frequently used platforms across a range of rheumatology organizations, journals, and patient groups include Facebook, Twitter, YouTube, Instagram, and YouTube ( Table 2 ). These trends show that rheumatology journals have embraced social media as an important outlet to disseminate research findings and broaden the scope of their reach.

Use of embedded social media links on rheumatology organizations’ homepages

Access date: November 11, 2022. ACR, American College of Rheumatology; EULAR, European Alliance of Associations for Rheumatology; PANLAR, Pan American League of Associations of Rheumatology; APLAR, Asia Pacific League of Associations for Rheumatology; AFLAR, African League Against Rheumatism.

Graphical and Visual Abstracts in Rheumatology Research and Care

A graphical or visual abstract is descriptive figure representing the key point(s) of a research article in an accessible style. Similar to the ubiquitous text-based abstract, these figures provide a means of summarizing long articles at a glance. In contrast with the text-based abstracts, these images are well suited for visual learners and for social media propagation. Imagery such as distinct shapes and colors may also serve as a visual cue to quickly distinguish between the many scientific papers with similar wording in the titles, rendering the article more memorable and recognizable.

While often used interchangeably, the terms “graphical abstract” and “visual abstract” represent distinct formats ( Figure 3 ). The term “graphical abstract” signifies a figure with a single panel depicting the core take-home message of an article, such as the mechanism of a novel drug or pathogen, a newly proposed molecular or physiological pathway, or the key result of a new intervention. Research methods and limitations are generally not described. It is steadily becoming more common for journal editors to solicit graphical abstracts as part of the submission process and to publish these figures in the journal alongside the article, juxtaposing the traditional, text-based abstract.

Graphical and visual abstracts for biomedical and clinical publications.

The term “visual abstract,” on the other hand, signifies the translation of the traditional text-based abstract into a visual format by arranging its components into a multi-panel figure or chart. Just as a traditional abstract may sometimes be broken down into sub-headings for background, methods, results, and conclusions, a visual abstract is sometimes comprised of panels with similar sub-headings. Some high-profile journals, such as those in the JAMA network, have an editorial graphics team that now creates professional visual abstracts in a strictly uniform, branded format for press releases and social media.

Graphical and visual abstracts are widely acknowledged to widen the reach and increase the impact of journal articles. For example, when we compare the results of tweeting a link to the article with versus without a visual abstract, larger numbers of impressions, retweets, and article visits are demonstrated in the case of the former. [ 18 ] While it is common knowledge that tweets with images receive more engagement in general, another study showed that simply including any relevant image such as a key figure from the article was inferior to tweeting a visual abstract. [ 19 ] Rheumatologists indicate support for social media promotion in general as well as visual abstracts in particular. [ 20 ] However, visual and graphical abstracts are currently uncommonly used in rheumatology. Seminars in Arthritis and Rheumatism is one of the few rheumatology journals that regularly encourages authors to make graphical abstracts, though most accepted articles do not construct them. Some Twitter users create their own graphical or visual abstracts exclusively for social media to amplify their article’s reach, particularly among lay audiences.

Rheum OnePagers (created by Dr. Mithu Maheswaranathan) is on Twitter and is an independent site that employs visual abstracts to effectively summarize research studies, guidelines, clinical content, and classification criteria. The site also tweets about differential diagnoses, interpretation of laboratory results, and management considerations for rheumatic diseases.

Whether a formal program or an informal post, those creating these graphics should balance the desire for consumer-friendly simplicity and impact with the risk of generating misinformation due to lack of nuance. [ 18 ] It is important to avoid biased oversimplifications that might arise from the perceived need for making the content accessible to a wider, non-medical audience, and endeavor to make it enticing for readers to read the full article, so that readers do not form a strong conclusion or judgment merely based on the pictorial information presented.

Twitter Journal Clubs in Rheumatology

Social media merged with the long-held medical tradition of journal club with the advent of Twitter-based journal clubs. Twitter-based journal clubs allow broad dissemination and discussion of scientific literature while including authors, collaborators, trainees, and patients. [ 21 ] Many medical specialties now have active Twitter journal clubs. [ 22 –26] Although most Twitter journal clubs are synchronous in real time, there are some that are asynchronous and occur over hours or days or have several sessions to accommodate different time zones. Twitter journal clubs are common among many specialties, and having an article discussed in a twitter journal club can boost Altmetrics and amplify research to a broad audience with post-publication peer review.

Rheumatology Twitter journal club (#RheumJC) was founded in 2015. [ 27 ] Other periodic rheumatology Journal Clubs on Twitter have included EMEUNET ( https://emeunet.eular.org/emeunet_journal_club.cfm ) as well as one dedicated to polymyalgia rheumatica and giant cell arteritis (#PMRGCAJC). Authors of papers are often invited to discuss the paper with other users. Some challenges for Twitter journal club include: choosing an impactful article worthy of lengthy discussion, amassing a sizable and engaged audience, broaching criticism and limitations while the author is present, and variable sophistication of the audience to research methods expertise.

FOAM in Rheumatology

Trainees across medicine have become increasingly reliant on FOAM resources. [ 28 ] In contrast with textbooks or academic publications, which have both financial and logistical barriers to access, FOAM resources are widely and immediately available for access through the internet. They may include print resources such as newsletters or blogs, audio resources such as podcasts, or visual media such as videos or infographics.

The FOAM movement has matured in various phases, which may be aptly illustrated by the evolution of such resources in rheumatology. In the early 2010s, a “Founders Wave” occurred in fields outside of rheumatology, including emergency medicine and critical care. These early adopters saw the potential of social media platforms and novel delivery mechanisms, such as blogs or podcasts, for disseminating information. Rheumatologists joined the movement in the Second Wave, “Adoption by Enthusiasts.” The first notable example was The Rheumatology Podcast , which is no longer published but inspired [ 29 ] many subsequent projects. This period also saw the creation of RheumNow, Healio Rheuminations, and The Evidence Based Rheumatology Podcast . These projects generally maintained the ethos and formats of the Second Wave. The past few years have seen a rapid transition into the Third Wave, or “Structure and Formalization”. Within a short time period, professional societies and academic publishing journals launched branded podcasts within their sphere, which aimed to disseminate their content. These podcast projects lent further legitimacy to the podcasting ecosystem and often, though not always, maintained the original freewheeling and conversational styles.

More recently, the movement has spilled over and democratized into what has been dubbed the Fourth Wave, or “Engagement and Activity by End Users.” This period has been characterized by omnidirectional engagement, whereby content creators publish podcasts and blogs that receive immediate feedback on social media platforms, such as Twitter. By engaging directly with their end users, content creators can both receive and respond directly to feedback. This feedback may not always be thoughtful or kind, which may discourage ongoing content creation. It also has many positive aspects, which include: (1) opportunities to clarify information when needed, (2) greater engagement from users who feel more connected to the media they consume, and (3) the formation of a broader social media community within rheumatology.

RheumMadness is another example of FOAM that was widely disseminated on Twitter and other venues, starting in 2020, and founded by Dr. David Leverenz. RheumMadness is modeled after NephMadness, a popular annual nephrology FOAM event. [ 30 , 31 ] Both RheumMadness and NephMadness follow the structure of the annual US college basketball national tournament called March Madness. Instead of competing basketball teams, RheumMadness matches high-impact research studies/topics against each other to determine the most impactful research study/topic of that year. Each match-up has a “scouting report” that summarizes the major findings and research methods. A “blue ribbon panel” of experts picks between each pair, in succession until the winner is chosen. There are multiple rounds, each lasting a few days, during which users on Twitter and other venues discuss each match-up. Prior to the blue ribbon panel picks, users can submit their picks (or “brackets”) to win a prize.

While not entirely focused on FOAM, rheumatology researchers and clinicians also interact regularly at some websites. These include ResearchGate ( https://www.researchgate.net/), which catalogs and provides metrics on research publications; MedNet ( https://www.themednet.org/), a crowdsourced question-and-answer platform of clinical scenarios; Web of Science ( https://access.clarivate.com/login?app=wos), which provides wide-ranging data on publication and peer review metrics; Figure 1 ( https://www.figure1.com/), which provides medical education from images of cases; and Doximity ( https://www.doximity.com/), a social network that is aimed at all physicians and also provides secure audio/video and fax services for clinical care.

Social Media and Patient Education in Rheumatology

Patients are active participants in social media and access various social media platforms to learn more about their disease, find physicians to care for them, and connect with people with similar diseases. The global reach of social media brought rheumatology patients together and made access to information about their disease (often rare in their immediate social community) easier and more convenient. For example, Cheryl Crow (@realcc) is an occupational therapist with rheumatoid arthritis who shares content about her experience with this disease, along with other educational content related to rheumatology, on Twitter and TikTok. Another example is #LupusChat (@Lupus_Chat), a regular Twitter event where people living with systemic lupus erythematosus discuss current issues related to that disease. Facebook has been a particularly popular platform among patient groups to host similar online communities. Clinicians can harness the power of social media to create platforms for patient education and connecting with patient groups. Organizations can also provide educational videos covering clinical and/or research topics on YouTube for independent patient use. On the Johns Hopkins Rheumatology channel, content ranges from disease overviews, research updates, and medication injection demonstrations.

Plain language summaries (PLS) are another way for researchers to connect with patients. The Annals of the Rheumatic Diseases selects particularly impactful publications to generate PLS, and other organizations collaborate to create PLS independently of the journals, most notably the coronavirus disease 2019 (COVID-19) Global Rheumatology Alliance (GRA). PLS serve the purpose of educating patients, but also are an important avenue to communicate research to other stakeholders.

Social Media as the Catalyst for Collaborative Research in Rheumatology

While social media has been a vehicle for the spread of both information and misinformation (e.g., hydroxychloroquine [ 32 , 33 ] ), researchers have leveraged the simplicity and accessibility of it to rapidly create impactful infrastructures and studies during an especially challenging time. Perhaps the most impactful way social media has positively influenced the rheumatology community during the pandemic was the initial organization of the COVID-19 GRA. [ 34 ]

The GRA is an international organization that runs a global registry collecting, analyzing, and disseminating data regarding the impact of COVID-19 on patients with rheumatic diseases. The genesis of the GRA came from a tweet by Dr. Leonard Calabrese, who quoted a tweet on March 11, 2020 from Dr. David Rubin (@IBDMD) that described the initiation of the Surveillance Epidemiology of Coronavirus Under Research Exclusion-Inflammatory Bowel Disease (SECURE-IBD; https://covidibd.org/ ) registry to collect COVID-19 outcomes data in those with IBD: “Totally smart thing to do – Are we doing this in RHEUM? I am unaware” – @LCalabreseDO, March 11, 2020 ( https://twitter.com/lcalabresedo/status/1237888297398972416).

Within hours, dozens of rheumatologists and epidemiologists on Twitter digitally crowdsourced real-time solutions addressing the feasibility, required infrastructure, and the necessary variables needed to rigorously collect and report these data. Remarkably, within 24 hours, the GRA became active with support from the ACR [ 35 ] and a workspace on Slack for internal discussions ( Figure 4 ). Over 250 members joined within the first week, and within 2 weeks, the registry went live (March 24, 2020 at www.rheum-covid.org).Furthermore, the final survey of the GRA registry was shared with the European Alliance of Associations for Rheumatology (EULAR), and this became active on March 27, 2020. Over 30,500 cases have been submitted to the GRA registry, resulting in over 30 original research publications, most of which have PLS for patients.

Achievements of the COVID-19 GRA organized via Slack workspace. COVID-19, coronavirus disease 2019; GRA, Global Rheumatology Alliance.

Social media has also served to disseminate surveys from dozens of studies, most of which have led to important observations. For example, the GRA heavily utilized social media to increase awareness of the GRA COVID-19 Patient Experience and Vaccine Surveys. The first patient-facing GRA survey investigated patient behavior early in the pandemic. [ 36 ] The second patient-facing GRA survey investigated clinical experience and uptake/attitudes related to the novel COVID-19 vaccines. These data identified that patients with rheumatic diseases had prolonged COVID-19 symptom duration [ 37 ] and low rate of disease flare post-vaccination, [ 38 ] and identified the need to reassure patients about vaccine efficacy and safety as critical variables in vaccine uptake. [ 39 ] The Vaccination Against COVID in Systemic Lupus (VACOLUP) exclusively used social media for participant recruitment. [ 40 ] The COVAD study is another survey related to vaccine experience for people with rheumatic diseases that successfully recruited over social media. [ 41 ] The ability of obtaining informed consent electronically without direct participant–investigator interactions was critical for the success of these studies during a period where those on immunosuppression may increase the risk of severe COVID-19 if person-to-person interactions were required. While there are some challenges related to sampling bias and unclear denominators, leveraging online platforms will likely reach a larger participant population than traditional paper/in-office surveys. It is inevitable that this will become the new norm as best practices are developed to rigorously execute primary research online. Finally, the COVID-19 Vaccine Responses in Patients with Autoimmune Disease (COVaRiPAD) is a multi-center prospective patient-oriented study that was partially forged through GRA collaborations. This study of people with immune-mediated inflammatory diseases before and after vaccine doses has investigated the impact of specific immunosuppressants on vaccine immunogenicity, [ 42 ] reactogenicity, [ 43 ] and booster effects. [ 44 ] Thus, social media can be the catalyst for research studies that lead to impactful findings and research funding.

Other Social Media Venues And Rheumatology

Other social media platforms rely more on photos or videos to disseminate content. TikTok, which launched in 2017, consists of an algorithm-based feed of short videos often including music and colorful graphics. Videos can have searchable hashtags and be reposted by other users often with corresponding commentary. Currently, only a few rheumatologists and rheumatology organizations have a presence on TikTok. This is likely due to lack of familiarity with the platform as well as the additional effort required for content generation on a video-based platform compared to text/image platforms such as Twitter. TikTok represents an opportunity for research dissemination, particularly as younger demographics continue to embrace the site. LinkedIn is another social media site where many rheumatology researchers have profiles and post career-related updates including publications. However, substantive discourse about research findings is relatively uncommon. Instagram is another popular social media site in which many rheumatology researchers and journals post images and content. However, discourse is again relatively uncommon and most rheumatology journals do not post content regularly. Mastodon has recently emerged as another social media site similar in structure and concept to Twitter. Mastodon is composed of many decentralized servers with more rigorous moderation than Twitter, but also allows cross-posting content to Twitter. However, uptake for Mastodon is currently small and users on different servers may experience difficulty in accessing content.

Social media has become an active venue to discuss, disseminate, form collaborations, and even collect data for research studies in rheumatology. Twitter is currently one of the most active venues for social media in rheumatology research, but others include podcasts, blogs, Facebook, and TikTok. While not everyone will embrace social media, researchers, patients, organizations, and stakeholders need to be aware of the wide landscape and opportunities that social media may offer. For example, social media is an excellent resource which enables the participation of not only rheumatologists and those casually seeking health care suggestions concerning this topic but also the interested segment of the wider general public (such as those interested in participating in surveys); participation may assume the form of, e.g., staying updated on the latest research findings in rheumatology by following rheumatology journals and digital opinion leaders. This can supplement traditional methods that rely on print journals, periodic visits to journal websites, research conferences, or tables of contents by email. Beyond staying on top of current research findings and research gaps, social media is also relevant for finding collaborators interested in similar research topics. Finally, research studies may actually recruit patients and collect data through electronic surveys via social media. Thus, social media is a powerful tool for innovation in rheumatology research that will likely continue to grow and evolve in its sophistication.

Funding statement: None declared.

Acknowledgements

The authors wish to thank the rheumatology social media community, in particular the COVID-19 Global Rheumatology Alliance and the #HCQbrigade.

Author Contributions

Conceptualizaion: JAS; Original draft: AC-R, ERG, AHJK, JWL, MSP, SES, KJY, JAS; Reviewing and editing: AC-R, ERG, JAS; Figures: AC-R and JAS; Final approval: AC-R, ERG, AHJK, JWL, MSP, SES, KJY, JAS.

Informed Consent

None declared.

Ethical Statement

Conflict of Interest

Dr. Kim is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant No. P30 AR073752), National Center for Advancing Translational Sciences (grant No. UL1 TR002345), Leona M. and Harry B. Helmsley Charitable Trust, Rheumatology Research Foundation, and National Multiple Sclerosis Society. Dr. Kim has received research support to Washington University from GlaxoSmithKline and Foghorn Therapeutics, and performed consultancy for Alexion Pharmaceuticals, ANI Pharmaceuticals, AstraZeneca, Aurinia Pharmaceuticals, Exagen Diagnostics, GlaxoSmithKline, Kypha, and Pfizer unrelated to this work. Dr. Kim is the inventor of patent No. 11029318 unrelated to this work. Michael Putman participates in clinical trials funded by Abbvie (SELECT-GCA) and AstraZeneca (MANDARA) and has received consulting payments from Novartis. Dr. Sattui is supported by the Rheumatology Research Foundation RISE Pilot Award and by the Bristol Myers Squibb Foundation Winn Career Development Award, outside of the submitted work. Dr. Sattui reports research support from AstraZeneca and consulting for Sanofi (not paid). Dr. Sparks is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant Nos R01 AR077607, P30 AR070253, and P30 AR072577), the R. Bruce and Joan M. Mickey Research Scholar Fund, and the Llura Gund Award for Rheumatoid Arthritis Research and Care. Dr. Sparks has received research support from Bristol Myers Squibb and performed consultancy for AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Gilead, Inova Diagnostics, Janssen, Optum, and Pfizer unrelated to this work. The funders had no role in the decision to publish or preparation of this manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of Harvard University, its affiliated academic health care centers, or the National Institutes of Health.

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Research Article

Social Media Release Increases Dissemination of Original Articles in the Clinical Pain Sciences

Affiliations Sansom Institute for Health Research, University of South Australia, Adelaide, Australia, Neuroscience Research Australia, Sydney, Australia

Affiliations Neuroscience Research Australia, Sydney, Australia, University of New South Wales, Sydney, Australia

* E-mail: [email protected]

Heidi G. Allen,
Tasha R. Stanton,
Flavia Di Pietro,
G. Lorimer Moseley

Published: July 17, 2013
https://doi.org/10.1371/journal.pone.0068914
Reader Comments

A barrier to dissemination of research is that it depends on the end-user searching for or ‘pulling’ relevant knowledge from the literature base. Social media instead ‘pushes’ relevant knowledge straight to the end-user, via blogs and sites such as Facebook and Twitter. That social media is very effective at improving dissemination seems well accepted, but, remarkably, there is no evidence to support this claim. We aimed to quantify the impact of social media release on views and downloads of articles in the clinical pain sciences. Sixteen PLOS ONE articles were blogged and released via Facebook, Twitter, LinkedIn and ResearchBlogging.org on one of two randomly selected dates. The other date served as a control. The primary outcomes were the rate of HTML views and PDF downloads of the article, over a seven-day period. The critical result was an increase in both outcome variables in the week after the blog post and social media release. The mean ± SD rate of HTML views in the week after the social media release was 18±18 per day, whereas the rate during the other three weeks was no more than 6±3 per day. The mean ± SD rate of PDF downloads in the week after the social media release was 4±4 per day, whereas the rate during the other three weeks was less than 1±1 per day (p<0.05 for all comparisons). However, none of the recognized measures of social media reach, engagement or virality related to either outcome variable, nor to citation count one year later (p>0.3 for all). We conclude that social media release of a research article in the clinical pain sciences increases the number of people who view or download that article, but conventional social media metrics are unrelated to the effect.

Citation: Allen HG, Stanton TR, Di Pietro F, Moseley GL (2013) Social Media Release Increases Dissemination of Original Articles in the Clinical Pain Sciences. PLoS ONE 8(7): e68914. https://doi.org/10.1371/journal.pone.0068914

Editor: Margaret Sampson, Children’s Hospital of Eastern Ontario, Canada

Received: October 4, 2012; Accepted: June 3, 2013; Published: July 17, 2013

Copyright: © 2013 Allen 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.

Funding: Tasha Stanton is supported by the Canadian Institutes of Health Research Postdoctoral Training Fellowship [ID 223354]; G. Lorimer Moseley is supported by the National Health & Medical Research Council Research Fellowship [ID 571090]. Flavia Di Pietro is supported by a Australian Government Postgraduate award. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: This experiment was conducted using www.bodyinmind.org . Web metrics of this website are used as evidence of the authors’ research social media reach. The authors therefore stand to benefit indirectly should this manuscript increase the reach of that website. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

Introduction

The impact of research is fundamentally dependent on how well it is disseminated to the end-user. Conventional routes of dissemination involve journal publications, conference presentations and, ultimately although often years later, textbooks. This model of dissemination requires the end-user to search for, or ‘pull’, the relevant knowledge from the literature base [1] . With regard to health and medical research, this approach might be ineffective because the end-users are often clinicians who do not subscribe to journals, nor attend conferences. The rise of open access publication reduces one barrier to effective dissemination by making literature freely available for all who wish to consult it, but it still relies on the end-user pulling out the relevant knowledge [2] – [3] .

The rapid rise in popularity of web logs (blogs) and social media sites such as Facebook and Twitter, has positioned them as critical tools with which to aid dissemination. Health and medical research is no exception - high profile journals such as the New England Journal of Medicine (NEJM) and the British Medical Journal (BMJ) have established cohesive digital strategies that incorporate both blogs and social media sites ( Table S1 ), presumably in the hope of improving the dissemination of knowledge. This approach contrasts with the pull approach insofar as it ‘pushes’ the knowledge to the end-user [1] . By having different blog and social media sites, journals allow the end-user to self-select the genre of knowledge they wish to receive. RSS (Really Simple Syndication) is another example of how users can self-select information. Although not a pure social media tool, RSS feeds enable the pushing of individualised information and blog contents. RSS permits some user interaction and information sharing.

The fundamental importance of a digital strategy is emphatically stressed by social media advocates [4] . Markers such as the number of ‘likes’, or the number of Facebook or Twitter followers are cited as measures of research impact, collectively captured by concepts such as ‘altmetrics’ [5] . We contend, however, that the most common altmetrics are not measuring impact, insofar as impact relates to the effect of research on clinical practice or thinking. Moreover, the definitions of various terms are not clear and they mean different things to different people. For the purposes of this experiment, we define the key concepts as set out in Figure 1 . We took ‘reach’ to be the number of people who have been alerted to the presence of a web page and have the opportunity to view it [6] . Reach reflects the number of people who could potentially see the blog, either directly because they subscribe to the blog through RSS feed or email alerts, or through following the blog on various social media sites for example Facebook, Twitter, LinkedIn, Google+ or ResearchBlogging. One step closer to impact is engagement, defined here as the number of people who view the web page and then do something in response to viewing it – for example they ‘like’ it, re-tweet it, or they share it with their friends. The concept of ‘virality’ attempts to capture a stronger level of engagement and a reflection of the propensity of the message to ‘go viral’. Here we use the percentage of engagers who then write a story on the post on Facebook or begin a new tweet. This distinction between terms is important because as few as 16% of Facebook followers actually read a new post and about 1% of people who see and ‘like’ a Facebook page actually comment on it or start a new story on it [7] – [8] .

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Key concepts concerning social media metrics and what is arguably true research impact – achieving shifting practice or thinking, as distinct from the conventional although controversial measure of research impact - citations. We took dissemination of the research article, as measured by the number of unique users who viewed the HTML or downloaded the PDF of the article, as the most proximal estimate of true impact that we could measure. As impact is likely to reflect a proportion of dissemination, so too does dissemination reflect a small proportion of the social media metrics commonly used to reflect impact. The results of our study show clearly that although social medial release increases dissemination, the social media metrics do not relate to dissemination, nor to citation count a year later.

https://doi.org/10.1371/journal.pone.0068914.g001

A substantial gap in our understanding of the link between social media and impact, is the effect that a social medial release about a research article has on dissemination of the article itself. Remarkably, despite the apparent acceptance of social media reach, engagement and virality being evidence of impact, there seems to be no empirical evidence to support this claim [9] – [10] . This observation was recently noted by Priem et al - ‘Researchers must ask if altmetrics really reflect impact, or just empty buzz’ [5] . We undertook a blinded, randomised repeated measures experiment to test the hypothesis that social media release of an original research article in the clinical pain sciences increases viewing and downloads of the article, thereby demonstrating increased dissemination of the research and end-user behavioural change.

Sixteen original research articles were selected from the PLOS ONE group of journals ( Table S2 ). Inclusion criteria were: (i) relevance to the clinical pain sciences; (ii) of interest to the readership of our research group’s blog (bodyinmind.org), a readership that consists primarily of clinicians who work in a pain-related field; (iii) first published on-line between 01/01/2006 and 31/12/2011; (iv) not previously mentioned in a bodyinmind.org blog post.

Research articles were randomly allocated to four researchers in our group, each of whom wrote a blog post of around 500 words based on the original article, and which included a tag line directing the reader to the on-line version of the article for more information. All posts were released on a Tuesday (between 6 and 7 am) or between 11 pm Thursday and 2 am Friday, Australian Eastern Summer time. Other posts, not part of the current experiment, were also released during the experimental period (14/08/2011–02/02/2012). For each blog on a research article, two dates were randomly selected from all possible post-dates during the experimental period. Of the two dates, one was randomly selected as the release date and one as the control date ( Fig. 2 ). Each blog post was broadcast via ResearchBlogging.org, Facebook, Twitter and LinkedIn on the day of the blog post. The experiment was undertaken covertly, so there was no risk that end-users who knew the experiment was being conducted would visit the original article as a result of that knowledge.

Days of the social media release for each article (A–P) are shown by green cells. The randomly selected control days are shown by black cells. The period during which PLOS citation tracking was down and therefore data are missing, is shown by blue cells.

https://doi.org/10.1371/journal.pone.0068914.g002

The primary outcome variables were rate of HTML views and PDF downloads over a seven day period. The former reflects some engagement with the target article by visiting it on the PLOS website. The latter reflects a higher level of engagement with the target article by adding it to a user library, presumably for future reference. Both outcome variables represent a behavioural change associated with the target article. Each primary outcome variable was assessed during four seven-day periods: the seven days before and after the release date, and the seven days before and after the control date.

Facebook statistics were provided by ‘Facebook page insights’ for 28 days post publication of the blog post. They are not broken down into individual days. Twitter comments and re-tweets were searched for manually and relied on the Twitter search engine to identify all mentions.

Statistical Analysis

We undertook a 2×2 repeated-measures ANOVA on each primary outcome variable. The first factor was ‘Date’ (two levels: release date or control date). The second factor was ‘Week’ (two levels: before date or after date). In order to maximise the likelihood of detecting an effect on each primary outcome variable, which we took to reflect different levels of dissemination, we did not correct for multiple measures and set α = 0.05.

Secondary Analyses - Relating Citations, HTML Views and PDF Downloads to Social Media Reach and Engagement

We calculated the relationship between the primary outcome variables and recognised measures of social media reach and social media engagement. We undertook two linear regressions with the increase in HTML views or PDF downloads as the dependent variable, and the following measures of social media reach and engagement as the independent variables:

The number of unique visitors who were alerted to the blog post and had the opportunity to view it.

Engagement.

The number of unique people who liked, commented on, or shared the blog post on www.bodyinmind.org , Facebook, Twitter or LinkedIn.

The percentage of unique viewers who then created a story from the blog post on Facebook, twitter, or blogged about it separately.

We investigated whether social media reach or engagement related to a conventional measure of impact - citation count, as provided by Scopus. We did this using a third linear regression, with reach and engagement as regressors, and citation count at 03/09/2012 as the dependent variable. We also investigated whether HTML views or PDF downloads related to citations by correlating citation count at 03/09/2012 with total HTML views and total PDF downloads at the end of the week after the social medial release.

We tested whether there was a ‘blogger effect’ (ie, do some blogger’s posts have a greater impact than others?) by first calculating the difference in the change or rate of increase in the primary outcome variables between the social media release date and the control date. We called this the blog effect. We then compared the blog effect between reviewers using a Kruskal-Wallis test. Finally, we tested whether there was an ‘age effect’ (ie, is there an effect of the age of the article on our outcome variables?) by relating the blog effect to the days between publication of the article and the social media release.

No correction was applied for multiple measures because these were secondary and therefore exploratory, hypothesis-generating analyses.

Over the 18-week study period, the blog (bodyinmind.org) had an average of 2585 unique views per week. Each post was viewed a mean (SD) of 507 (160) times in the week following publication. In the 28 days after publication, a mean (SD) of 693 (135) unique visitors saw the post in their Facebook newsfeed; 35 (16) unique visitors clicked on each post; 6 (4) unique visitors created a like, comment or share from the post. Of the total number of unique visitors who saw the post on Facebook, 0.93% (0. 66%) created a story from it.

The rate of HTML views was higher during the second week than during the first, regardless of the date. That is, there was a main effect of Week on HTML views (F(1,15) = 6.27, p = 0.024). The rate of HTML views was also higher either side of the social media release than it was either side of the control date (main effect of Date on HTML views – F(1,15) = 7.39, p = 0.016). However, visual inspection of the data ( Fig. 3 ) show that these main effects were driven to a large extent by an interaction, such that the social media release was associated with a larger increase in the rate of HTML views than the control date was (Week x Date interaction: F(1,15) = 7.39, p 0.016). The mean ± SD rate of HTML views in the week after the social media release was 18±18 per day, whereas the rate during the other three weeks was no more than 6±3 per day ( Fig. 3 ), which equates to an effect size (Cohen’s d) of 0.9. That is, in the week after the social media release, about 12 people per day viewed the research article as a result of the social medial release.

The rate of HTML views of each research article on which a social media release was based, for the week either side of two randomly selected dates. The data for the control date are on the left and the data for the social medial release date are on the right. Note the systematic increase in rate of HTML views from the week before the social media release to the week after it.

https://doi.org/10.1371/journal.pone.0068914.g003

PDF Downloads

The results for PDF downloads reflected those for HTML views: The rate of PDF downloads was higher during the second week than during the first (main effect of Week – F(1,15) = 10.83, p = 0.005) and higher either side of the social media release than it was either side of the control Date (main effect of date – F(1,15) = 6.57, p = 0.022). Again, these effects were driven by an interaction, such that the social media release was associated with a larger increase in the rate of PDF downloads than the control date was (Week x Date interaction: F(1,15) = 14.74, p = 0.002). The mean ± SD rate of PDF downloads in the week after the social media release was 4±4 per day, whereas the rate during the other three weeks was less than 1±1 per day ( Fig. 4 ), which equates to an effect size (Cohen’s d) of 1. That is about 3 people per day downloaded the research article as a result of the social media release.

The rate of PDF downloads of each research article on which a social media release was based, for the week either side of two randomly selected dates. The data for the control date are on the left and the data for the social medial release date are on the right. Note the systematic increase in rate of PDF downloads from the week before the social media release to the week after it.

https://doi.org/10.1371/journal.pone.0068914.g004

How well do reach, engagement and virality of the social media release relate to HTML views and PDF downloads of the research article?

Engagement was 5.3% of reach and virality was 0.9% of engagement. None of the social media metrics related to the increase in rate of HTML views of the research article (p = 0.947 for reach; p = 0.809 for engagement; p = 0.544 for virality), nor to the increase in PDF downloads of the research article (p = 0.323 for reach; p = 0.864 for engagement; p = 0.934 for virality). The only relationship that approached significance was that between the number of HTML views of the blog post and PDF downloads (p = 0.09).

Relationship between Reach, Virality and Citations

There was no relationship between citations on Scopus about one year after publication and any of the social media metrics (p>0.68 for all). Total PDF downloads at the end of the week after social media release related to total HTML views at the same time (Pearson r = 0.72; p = 0.002). Interestingly, citations at 03/09/2012 related to total PDF downloads (Pearson r = 0.51; p = 0.045) but not to total HTML views (Pearson r = 0.06; p = 0.826).

Was there a ‘blogger’ Effect?

One blogger wrote seven posts, two wrote four posts and one wrote one post. There was no difference between bloggers for either HTML views or PDF downloads (p>0.88 for both).

Was there an Article Age Effect?

The age of the article at the time of blogging was not related to the rate of HTML views, or the rate of PDF downloads, during the any of the four one-week periods (p>0.71 for all). The blog effect was not affected by the age of the article at the time of blogging (p = 0.28).

We hypothesised that social media release of an original research article in the clinical pain sciences increases viewing and downloads of the article. The results support our hypothesis. In the week after the social media release, there were about 12 extra views of the HTML of the research article per day, and 3 extra downloads of the article itself per day, that we can attribute to the social media release. The effects were variable between articles, showing that multiple factors mediate the effect of a social media release on our chosen outcome variables. Although the absolute magnitude of the effect might be considered small (about 0.01% of people we reached were sufficiently interested to download the PDF), the effect size of the intervention was large (Cohen’s d >0.9 for both outcomes). The effect of social media release was probably smaller for our site, which is small, young and specialised, than it would be for sites with greater gravitas, for example NEJM or BMJ or indeed, PLOS.

Relationship between Reach and Impact

The idea of social media reach is fairly straightforward - it can be considered as the number of people in a network, for example the number of Facebook friends or Twitter followers. A blog may have 2,000 Facebook ‘likes’, 700 Twitter followers and 300 subscribers - a reach of three thousand people. Impact is less straightforward. As depicted in Figure 1 , the various definitions of social media each reflects a substantially larger population than our most proximal measure of impact – HTML views and PDF downloads of the original article. One might suggest that impact should reflect some sense of engagement with the material, for example the number of people within a network who make a comment on a post. From a clinical pain sciences perspective, change in clinical practice or clinician knowledge would be clear signs of impact, but such metrics are very difficult to obtain. Perhaps this is part of the reason that researchers are using, we believe erroneously, social media reach as a measure of social media impact.

There are now several social media options that researchers integrate into their overall ‘impact strategy’, for example listing their research on open non-subscription sites such as Mendeley, and joining discussions about research on social media sites such as Twitter and on blogs. Certainly, current measures of dissemination, most notably citations of articles or the impact factor of the journals in which they are published, do not take into account the social media impact of the article. New measurements, such as altmetrics [5] and article-level metrics such as those provided by PLOS [11] , aim to take into account the views, citations, social network conversations, blog posts and media coverage in an attempt to analyse the influence of research across a global community. There is merit in this pursuit, but, although our study relates to clinical pain sciences research, our results strongly suggest that we need to be careful in equating such measures with impact or influence, or using them as a surrogate for dissemination. Indeed, not even virality, which estimates the propensity of an item to ‘go viral’, was related with HTML views or PDF downloads. This is very important because our results actually suggest that we may be measuring the wrong thing when it comes to determining the social media impact of research. That is, we showed a very clear effect of the social media release on both HTML views and PDF downloads of the target article. However, we did not detect any relationship between either outcome and the social media metrics we used. The only variable that related to either outcome was the number of HTML views, of the original blog post, in the week after social media release. It seems clear then, that it is not the total number of people you tell about your study, nor the number of people they tell, nor the number of people who follow you or who re-tweet your tweets. In fact, it appears that we are missing more of how the release improves dissemination than we are capturing.

The final result, that citation count did not relate to any social media measures, casts doubt over the intuitively sensible idea that social media impact reflects future citation-related impact [12] . We used the Scopus citation count, taken almost 9 months after the completion of the experimental period, and 1–2 years after the publication date of the target articles, as a conventional measure of impact. There was no relationship between citation count and our measures of social media reach or virality. One must be cautious when interpreting this result because citation count so soon (1–2 years) after publication might be unlikely to capture new research that was triggered by the original article – although, importantly, journal impact factors are calculated on the basis of citations in the two years after publication. Suffice here to observe that the apparent popularity of an article on social media does not necessarily predict its short-term citation count.

Although this is the first empirical evaluation of social media impact in the clinical pain sciences and we have employed a conservative and robust design, we acknowledge several limitations. Social media dissemination in the clinical sciences relies on clinicians having access to, and using, social media. It will have no effect for those who do not use the web and who rely on more traditional means of dissemination - ‘pulling’ the evidence. Although there was an increase in HTML views and PDF downloads as a result of social media dissemination, we do not know if people read the article or whether it changed their practice. We presumed that a portion of those who viewed the HTML version of the article would then go onto download it, however our data suggest that a different pattern of access is occurring. Unfortunately, our data do not allow us to determine whether the same people both viewed the HTML and downloaded the article PDF or whether different people viewed the HTML and downloaded the article PDF. Downloading a PDF version of a paper does not necessarily imply that they would later read it, but it does increase the probability of such.

Citations and impact factors measure the impact within the scientific community whereas views by social media will also include interested clinicians and laypeople and, as such, measure uptake by different audiences. Although we used a variety of different social media platforms to disseminate to as wide an audience as possible, we do not know who the audience is - we can only surmise that they are a mixture of researchers, clinicians, people in pain and interested laypeople. Further, each social media strategy comes with inherent limitations in regards to data collection of usage statistics related to a blog post. Gathering Facebook and Twitter statistics for each article is still cumbersome and is probably not always accurate. The risk in using search engines to gather data is that there is no way of knowing whether all the data have been identified. For Twitter there is no way to retrospectively calculate the number of re-tweets accurately over a longer period retrospectively for each post [13] . As a result, our Twitter data is a best estimate and my have underestimated the true values but, critically, we would expect this effect to be unrelated to our blog post and therefore not impact on our findings. Regarding Facebook, shares, likes and comments are grouped as one statistic but in reality only shares and comments show engagement with the post and indicate that people are more likely to have read it. Regarding LinkedIn, the only available data was the number of members of the BodyInMind group and as such, we have no way of knowing how many viewed the actual blog post.

The blog, BodyInMind.org, through which the original blog posts of PLoS ONE articles were released, experienced a technical interruption half-way through the experiment. In spite of an attempt by PLOS to retrieve the statistics, approximately five days of data were lost on several of the blog posts. This also meant that additional data on traffic, such as percentage of traffic for each blog post from external sources such as Facebook, Twitter, LinkedIn and ResearchBlogging could not be measured during this period. Critically and fortuitously, this period did not coincide with data collection weeks (see Fig. 2 ). PLOS indicated that this technical problem has now been fixed, but similar problems may arise in the future and present an ongoing risk to studies such as ours. Although disconcerting for those keenly following social media data, this problem would be very unlikely to have affected our primary outcomes because none of our dates fell within the period that was affected.

Social influence can produce an effect whereby something that is popular becomes more popular and something that is unpopular becomes even less popular [14] . It seems possible that articles on BodyInMind.org were shared because the site is popular among a discrete community and not because the article itself merited circulation. This possibility does not confound our main result but it adds a possible argument to the common objective of making a blog more popular as a device to boost social media impact of individual posts. Finally, our study relied on the target articles being freely available to the public. Many journals are not open access, particularly those in the clinical pain sciences. Therefore, we must be cautious extrapolating our results to subscription only access journals.

In conclusion, our results clearly support the hypothesis that social media can increase the number of people who view or download an original research article in the clinical pain sciences. However, the size of the effect is not related to conventional social media metrics, such as reach, engagement and virality. Our results highlight the difference between social media reach and social media impact and suggest that the latter is not a simple function of the former.

Supporting Information

Example of Journals social media use as viewed 9 August 2012.

https://doi.org/10.1371/journal.pone.0068914.s001

PLoS articles dissemination and corresponding changes in views and downloads. (*)Reach = The number of unique people who have seen the post in their newsfeed or on the Body in Mind page. Figures are for the first 28 days after a posts’s publication only. None of the posts were promoted via Facebook advertisements. (†)Engaged Users = number of unique people who have clicked on your post. Figures are for the first 28 after a post’s publication only. (‡)Talking about this = the number of unique people who have created a ‘story’ (a like, comment on, or share) from the post. Figures are for the first 28 days after publication only. (§)Virality = the percentage of people who have created a story from the post out of the total number of unique people who have seen it. (**)Number of tweets as of 28 March 2012.

https://doi.org/10.1371/journal.pone.0068914.s002

Author Contributions

Conceived and designed the experiments: GLM HGA TRS FDP. Performed the experiments: GLM HGA TRS FDP. Analyzed the data: GLM. Wrote the paper: GLM HGA TRS FDP.

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Social media for research dissemination

Wednesday, May 27, 2020 | By: Amanda Barnes

Being able to disseminate your research beyond a small circle of scholars is now a condition of most research grants. And even if you aren’t in an academic institute and your research project doesn’t depend on grant funding, it is probably being produced in order to influence opinion, policy or decision-making. So it’s important that research findings are disseminated to the audiences they’re intended to influence.

A lot of researchers ask if it’s worth investing time in social media, questioning if anything of value can be said in just 140 characters on Twitter. Journal articles are 3,000 to 8,000 words and books contain up to 80,000 words, so the limitations of social media can seem unappealing.

Not just for trivia and narcissism

And Twitter-phobes observing social-media pronouncements from the likes of Donald Trump, could be forgiven for thinking that social media has become little more than a platform for narcissists. But social media isn’t just for trivia and naked self-promotion.

Used well, social media is a very effective tool for disseminating research, both within and beyond the academic community. At the time of writing, Facebook has a billion users, and their average age is around 45. Twitter has 320 million users, including many of precisely those influencers and policy-makers whose opinions and decisions you may want your research findings to help shape.

Reaching policy-makers

Different online tools, such as tweets, blogs and Facebook postings, can be used in combination to get research findings out to a large audience. And it’s also a good way to raise the profile of a research project and build its authority. A university researcher who was a client of ours was invited to contribute an interview to the BBC World Service because a journalist saw a tweet about a blog he published. Not only was the blog read by policy-makers who could take his findings on board but would never read a 5,000-word journal article, his radio interview also reached millions of pairs of ears all over the world.

There are five things you should take into account when considering how to use social media for your research dissemination:

It’s important to define the purposes of using social media platforms, as well as how to use them to achieve that purpose
You shouldn’t try to be everywhere or you won’t be able to sustain the resources to maintain them all. There are so many platforms now: Twitter, Facebook, Linked I n, Medium, Instagram . . . the list goes on. Not all will be right for your research dissemination. So you need to look at where the communities you want to reach are engaging on social media and focus there.
You can’t do social media by half-measures. You need to reach a minimum critical mass and engage on a regular basis in order to make an impact.
You need to figure out how to combine the platforms you use and make sure that together they’ll earn their keep.
There aren’t any hard and fast rules. Where you concentrate your efforts and how you use your social media platform depends on your purpose, the resources you have at your disposal, and most of all on the social media habits of the communities you want to reach. The good thing is that you can easily experiment.

Don’t just launch into using social media for your research dissemination without putting a plan in place first. Make sure you’ve identified exactly what you want to get out of it. Using your objectives as a starting point, draw up a social media strategy that identifies what platforms you’re going to focus on, how you’re going to use it, what resources you need to put in place to sustain the strategy, and how you’re going to measure how well your plan is working.

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Dissemination, Media & Reports
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The Use of Social Media for Dissemination of Research Evidence to Health and Social Care Practitioners: Protocol for a Systematic Review

Helen A Baxter

Sophia Quirke-McFarlane

Fiona J Leggat

Martha Powell

Conclusions

Trial Registration

International Registered Report Identifier (IRRID)

Introduction

Eligibility

Summary of inclusion and exclusion criteria.

Information Sources

Search Strategy

Data Management

Selection of Studies

Data Extraction

Outcome Data

Risk of Bias and Quality Rating of Evidence

Data Synthesis

Confidence in Cumulative Evidence

Dissemination Plan

Acknowledgments

Abbreviations

This paper is in the following e-collection/theme issue:

The Use of Social Media for Dissemination of Research Evidence to Health and Social Care Practitioners: Protocol for a Systematic Review

Corresponding Author:

Introduction

Eligibility

Summary of inclusion and exclusion criteria.

Information Sources

Search Strategy

Data Management

Selection of Studies

Data Extraction

Outcome Data

Risk of Bias and Quality Rating of Evidence

Data Synthesis

Confidence in Cumulative Evidence

Dissemination Plan

Acknowledgments

Data Availability

Conflicts of Interest

Abbreviations

LifeScienceToday

Social beings on social media

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Demba Kandeh

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Social media for research discourse, dissemination, and collaboration in rheumatology

Introduction

Organic Tweets about Rheumatology Research

Tweetorials about Rheumatology Publications

Live-Tweeting Rheumatology Conferences

Benefits of Live-Tweeting Rheumatology Conferences

Best Practices for Live-Tweeting Rheumatology Conferences

Social Media and Rheumatology Journals

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Twitter Journal Clubs in Rheumatology

FOAM in Rheumatology

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Other Social Media Venues And Rheumatology

Acknowledgements

Supplementary Materials

Journal and Issue

Social Media Release Increases Dissemination of Original Articles in the Clinical Pain Sciences

Introduction

Statistical Analysis

Secondary Analyses - Relating Citations, HTML Views and PDF Downloads to Social Media Reach and Engagement

Engagement.

PDF Downloads

How well do reach, engagement and virality of the social media release relate to HTML views and PDF downloads of the research article?

Relationship between Reach, Virality and Citations

Was there a ‘blogger’ Effect?

Was there an Article Age Effect?

Relationship between Reach and Impact

Supporting Information

Author Contributions

Social media for research dissemination