Communities of shared interests and cognitive bridges: the case of the anti-vaccination movement on Twitter

This paper presents an analysis of the anti-vaccination movement’s referencing of research articles on the topic of vaccination in the social media network Twitter. Drawing on the concept of bibliographic coupling, the paper demonstrates how Twitter users can be coupled based on articles mentioned on Twitter. The sample applied consists of 113 open access journal articles. The combination of tweeter coupling with the respective stance of Twitter accounts vis-à-vis vaccination makes possible the creation of a network graph of tweeters mentioning this corpus of articles. In addition to a common interest in the scientific literature, the findings show distinct communities of shared interests within the anti-vaccination movement, and demonstrate that tweeter coupling can be used to map these distinctive interests. The emergence of Twitter accounts serving as cognitive bridges within and between communities is noted and discussed with regard to their relative positions in the network. This paper’s results extend the knowledge on the application of altmetric data to study the interests of non-scientific publics in science; more specifically, it adds to the understanding of the potentials of open science and science–society interactions arising from increased access by non-scientists to scientific publications.

[1]  M. M. Kessler Bibliographic coupling between scientific papers , 1963 .

[2]  M. Castells The rise of the network society , 1996 .

[3]  P. Geurts,et al.  Forces and functions in scientific communication: an analysis of their interplay , 1997 .

[4]  Blaise Cronin,et al.  Invoked on the Web , 1998, J. Am. Soc. Inf. Sci..

[5]  David Harel,et al.  A Fast Multi-scale Method for Drawing Large Graphs , 2000, Graph Drawing.

[6]  Peter R. Monge,et al.  Theories of Communication Networks , 2003 .

[7]  R. M. Wolfe,et al.  Vaccine Criticism on the World Wide Web , 2005, Journal of medical Internet research.

[8]  Sheila Jasanoff,et al.  Transparency in Public Science: Purposes, Reasons, Limits , 2006 .

[9]  Felix Stalder,et al.  Manuel Castells: The Theory of the Network Society , 2006 .

[10]  Dangzhi Zhao,et al.  Evolution of research activities and intellectual influences in information science 1996–2005: Introducing author bibliographic-coupling analysis , 2008 .

[11]  Andreas Strotmann,et al.  Evolution of research activities and intellectual influences in information science 1996-2005: Introducing author bibliographic-coupling analysis , 2008, J. Assoc. Inf. Sci. Technol..

[12]  M. Castells Communication Power: Mass Communication, Mass Self-Communication and Power Relationships in the Network Society , 2009, Media and Society.

[13]  Ben Shneiderman,et al.  Analyzing Social Media Networks with NodeXL: Insights from a Connected World , 2010 .

[14]  S. J. Bean Emerging and continuing trends in vaccine opposition website content. , 2011, Vaccine.

[15]  P. Weingart Science, the Public and the Media – Views from Everywhere , 2011 .

[16]  M. Castells ”Networks of Outrage and Hope. Social Movements in the Internet Age”. , 2019 .

[17]  A. Kata Anti-vaccine activists, Web 2.0, and the postmodern paradigm--an overview of tactics and tropes used online by the anti-vaccination movement. , 2012, Vaccine.

[18]  P. Gerbaudo Tweets and the Streets: Social Media and Contemporary Activism , 2012 .

[19]  Tommaso Venturini,et al.  Building on faults: How to represent controversies with digital methods , 2012, Public understanding of science.

[20]  Shalin Hai-Jew Analyzing Social Media Networks with NodeXL: Insights from a Connected World , 2012 .

[21]  Mike Thelwall,et al.  Tweeting Links to Academic Articles , 2013 .

[22]  J. V. van Dijck,et al.  Understanding Social Media Logic , 2013 .

[23]  Dan M. Kahan,et al.  A Risky Science Communication Environment for Vaccines , 2013, Science.

[24]  Pablo Barberá How Social Media Reduces Mass Political Polarization. Evidence from Germany, Spain, and the U.S. , 2014 .

[25]  Cornelius Puschmann,et al.  (Micro)Blogging Science? Notes on Potentials and Constraints of New Forms of Scholarly Communication , 2014 .

[26]  Dietram A. Scheufele,et al.  Science communication as political communication , 2014, Proceedings of the National Academy of Sciences.

[27]  Vincent Larivière,et al.  Astrophysicists on Twitter: An in-depth analysis of tweeting and scientific publication behavior , 2014, Aslib J. Inf. Manag..

[28]  Éric Archambault,et al.  Proportion of Open Access Papers Published in Peer-Reviewed Journals at the European and World Levels—1996-2013 , 2014 .

[29]  D. Kahan Vaccine Risk Perceptions and Ad Hoc Risk Communication: An Empirical Assessment , 2014 .

[30]  Richard Rogers,et al.  Digital Methods for Web Research , 2015 .

[31]  Rodrigo Costas,et al.  Interpreting "altmetrics": viewing acts on social media through the lens of citation and social theories , 2015, ArXiv.

[32]  S. Haustein,et al.  Characterizing Social Media Metrics of Scholarly Papers: The Effect of Document Properties and Collaboration Patterns , 2015, PloS one.

[33]  Dietram A. Scheufele,et al.  Selecting Our Own Science , 2015 .

[34]  Ali Ghazinejad,et al.  Who Tweets about Science? , 2015, ISSI.

[35]  S. Leonelli,et al.  Sticks and carrots: encouraging open science at its source , 2015, Geo : geography and environment.

[36]  Daniel Spichtinger,et al.  Sticksandcarrots: encouraging open science at its source: Encouraging open science at its source , 2015 .

[37]  Winni Johansen,et al.  Organizations, Stakeholders, and Intermediaries: Towards a General Theory , 2015 .

[38]  Rodrigo Costas Identifying Twitter audiences: who is tweeting about scientific papers? , 2015 .

[39]  J. Leask Should we do battle with antivaccination activists? , 2015, Public health research & practice.

[40]  David Gunnarsson Lorentzen,et al.  Twitter conversation patterns related to research papers , 2016, Inf. Res..

[41]  Yang Feng Are you connected? Evaluating information cascades in online discussion about the #RaceTogether campaign , 2016, Comput. Hum. Behav..

[42]  Thed N. van Leeuwen,et al.  Using Altmetrics for Contextualised Mapping of Societal Impact: From Hits to Networks , 2016 .

[43]  Bernd Blöbaum Key Factors in the Process of Trust. On the Analysis of Trust under Digital Conditions , 2016 .

[44]  Thed N. van Leeuwen,et al.  SSH & the City. A Network Approach for Tracing the Societal Contribution of the Social Sciences and Humanities for Local Development , 2016, ArXiv.

[45]  M. Moran,et al.  What makes anti-vaccine websites persuasive? A content analysis of techniques used by anti-vaccine websites to engender anti-vaccine sentiment , 2016 .

[46]  C. Rodrigo Discussing practical applications for altmetrics: social media profiles for African, European and North American publications , 2016 .

[47]  Stefanie Haustein,et al.  Grand challenges in altmetrics: heterogeneity, data quality and dependencies , 2016, Scientometrics.

[48]  Tim Wu The Attention Merchants: The Epic Scramble to Get Inside Our Heads , 2016 .

[49]  Scott Counts,et al.  Understanding Anti-Vaccination Attitudes in Social Media , 2016, ICWSM.

[50]  Lars Guenther,et al.  Science communication and the issue of trust , 2016 .

[51]  Iain G. Johnston,et al.  The State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey , 2016, EBioMedicine.

[52]  Gianluca Stringhini,et al.  The web centipede: understanding how web communities influence each other through the lens of mainstream and alternative news sources , 2017, Internet Measurement Conference.

[53]  Rodrigo Costas,et al.  The unbearable emptiness of tweeting—About journal articles , 2017, PloS one.

[54]  R. Costas,et al.  Beyond the dependencies of altmetrics : conceptualizing ‘ heterogeneous couplings ’ between social media and science , 2017 .

[55]  Kim Holmberg,et al.  Highly tweeted science articles: who tweets them? An analysis of Twitter user profile descriptions , 2017, Scientometrics.

[56]  M. Wacha,et al.  The State of OA: A large-scale analysis of the prevalence and impact of Open Access articles , 2017 .

[57]  Brian G. Southwell,et al.  Promoting Popular Understanding of Science and Health Through Social Networks , 2017 .

[58]  P. Hotez,et al.  Public Health and Economic Consequences of Vaccine Hesitancy for Measles in the United States , 2017, JAMA pediatrics.

[59]  Davide Bennato The shift from public science communication to public relations. The Vaxxed case , 2017 .

[60]  Mike S. Schäfer How Changing Media Structures Are Affecting Science News Coverage , 2017 .

[61]  Vincent Miller Phatic culture and the status quo , 2017 .

[62]  #Vaccination: Identifying Influencers in the Vaccination Discussion on Twitter through Social Network Visualisation , 2017 .

[63]  M. Bucchi Credibility, expertise and the challenges of science communication 2.0 , 2017, Public understanding of science.

[64]  N. Robinson-García,et al.  Using Altmetrics for Contextualised Mapping of Societal Impact: From Hits to Networks , 2017 .

[65]  Ben Shneiderman,et al.  Classifying Twitter Topic-Networks Using Social Network Analysis , 2017 .

[66]  Vincent Larivière,et al.  Scholarly use of social media and altmetrics: A review of the literature , 2016, J. Assoc. Inf. Sci. Technol..

[67]  M. Moten,et al.  Measles: is it still a threat? , 2018, The British journal of general practice : the journal of the Royal College of General Practitioners.

[68]  M. Thelwall,et al.  Academic information on Twitter: A user survey , 2018, PloS one.

[69]  Joshua A. Tucker,et al.  Social Media, Political Polarization, and Political Disinformation: A Review of the Scientific Literature , 2018 .

[70]  Castells in Africa: Universities and Development , 2018 .

[71]  Jure Leskovec,et al.  Community Interaction and Conflict on the Web , 2018, WWW.

[72]  Fereshteh Didegah,et al.  Investigating the quality of interactions and public engagement around scientific papers on Twitter , 2018, J. Informetrics.

[73]  Rodrigo Costas,et al.  General discussion of data quality challenges in social media metrics: Extensive comparison of four major altmetric data aggregators , 2018, PloS one.

[74]  James Williams Stand Out of Our Light : Freedom and Resistance in the Attention Economy , 2018 .

[75]  Grant Blank,et al.  The echo chamber is overstated: the moderating effect of political interest and diverse media , 2018 .

[76]  Ubaldo Cuesta-Cambra,et al.  An analysis of pro-vaccine and anti-vaccine information on social networks and the internet: Visual and emotional patterns , 2019, El Profesional de la Información.

[77]  Adrián A. Díaz-Faes,et al.  Towards a second generation of ‘social media metrics’: Characterizing Twitter communities of attention around science , 2019, PloS one.

[78]  Van Schalkwyk,et al.  New potentials in the communication of open science with non‐scientific publics: The case of the anti‐vaccination movement , 2019 .

[79]  Stefanie Haustein,et al.  How much research shared on Facebook is hidden from public view? A comparison of public and private online activity around PLOS ONE papers , 2019, ArXiv.

[80]  A. Crooks,et al.  Examining Emergent Communities and Social Bots Within the Polarized Online Vaccination Debate in Twitter , 2019, Social Media + Society.

[81]  Juan Pablo Alperin,et al.  How much research shared on Facebook happens outside of public pages and groups? A comparison of public and private online activity around PLOS ONE papers , 2019, Quantitative Science Studies.

[82]  Jin-Cheon Na,et al.  A comparative analysis of Twitter users who Tweeted on psychology and political science journal articles , 2019, Online Inf. Rev..

[83]  Mike Thelwall,et al.  Who shares health and medical scholarly articles on Facebook? , 2020, Learn. Publ..