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

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Abstract

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.

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Notes

  1. Using a web crawler to identify anti-vaccination accounts followed by manual verification of the Twitter accounts, Van Schalkwyk (2019a) identified 658 anti-vaccination accounts that mention open access journal articles on the topic of vaccination and autism. From the manual verification process, several pro-science accounts were also identified. Anti-vaccination Twitter accounts were defined as those that regularly tweet or retweet content to persuade others of the dangers of vaccines, while pro-science accounts were those that (re)tweet to defend the consensus position of science, that is, that vaccines are effective in combatting infectious diseases and pose no material health risks to those who are vaccinated. The data on stance, while not comprehensive in the sense that it provided classifications for all tweeters in the tweeter coupling network, was added to NodeXL to provide additional information in the analysis of the network.

References

  • Archambault, E., Amyot, D., Deschamps, P., Nicol, A., Provencher, F., Rebout, L., et al. (2014). Proportion of open access papers published in peer-review journals at the European and World Levels 1996–2013. Brussels: European Commission. Retrieved from http://science-metrix.com/sites/default/files/science-metrix/publications/d_1.8_sm_ec_dg-rtd_proportion_oa_1996-2013_v11p.pdf.

  • Barberá, P. (2015). How social media reduces mass political polarization: Evidence from Germany, Spain, and the US. Paper presented at the 2015 APSA Conference, 3–6 September, San Francisco. Retrieved from http://tinyurl.com/kyrqfe7.

  • BBC. (2019). Measles: Four European nations lose eradication status. BBC News. https://www.bbc.com/news/health-49507253.

  • Bean, S. (2011). Emerging and continuing trends in vaccine opposition website content. Vaccine, 29, 1874–1880.

    Article  Google Scholar 

  • Bennato, D. (2017). The shift from public science communication to public relations: The Vaxxed case. Journal of Science Communication, 16(2), 1–11.

    Article  Google Scholar 

  • Blöbaum, B. (2016). Key factors in the process of trust: On the analysis of trust under digital conditions. In B. Blöbaum (Ed.), Trust and communication in a digitized world: Models and concepts of trust research (pp. 3–26). Heidelberg: Springer.

    Chapter  Google Scholar 

  • Bucchi, M. (2018). Credibility, expertise and challenges of science communication 2.0. Public Understanding of Science, 26(8), 890–893. https://doi.org/10.1177/0963662517733368.

    Article  Google Scholar 

  • Castells, M. (1996). The rise of the network society. Oxford: Blackwell.

    MATH  Google Scholar 

  • Castells, M. (2009). Communication power. Oxford: Blackwell.

    Google Scholar 

  • Castells, M. (2015). Networks of outrage and hope: Social movements in the internet age (2nd ed.). London: Polity.

    Google Scholar 

  • CDC. (2013). CDC telebriefing on the National Immunization Survey, Vaccine for Children Program, and recent measles outbreaks in the US. Retrieved from https://www.cdc.gov/media/releases/2013/t0912_measles-outbreaks-data.html.

  • CDC. (2015). Transcript for CDC telebriefing: Measles in the United States, 2015. Retrieved from https://www.cdc.gov/media/releases/2015/t0129-measles-in-us.html.

  • Costas, R., De Rijcke, S., & Marres, N. (2017). Beyond the dependencies of altmetrics: Conceptualizing ‘heterogeneous couplings’ between social media and science. Paper presented at Altmetrics17: The dependencies of altmetrics. Toronto. Retrieved from Altmetrics: http://altmetrics.org/wp-content/uploads/2017/09/altmetrics17_paper_4-1.pdf.

  • Costas, R., Van Honk, J., Zahedi, Z., & Calero-Medina, C. (2016). Discussing practical applications for altmetrics: Social media profiles for African, European and North American publications. Presentation at the conference 3:AM, Bucharest, September 2016. https://doi.org/10.6084/m9.figshare.3980145.v1.

  • Cronin, B., Snyder, H., Rosenbaum, H., Martinson, A., & Callahan, E. (1998). Invoked on the web. Journal of the American Society for Information Science, 49(14), 1319–1328.

    Article  Google Scholar 

  • Cuesta-Cambra, U., Martínez-Martínez, L., & Niño-González, J.-I. (2019). An analysis of pro-vaccine and anti-vaccine information on social networks and the internet: Visual and emotional patterns. El profesional de la información, 28(2), e280217. https://doi.org/10.3145/epi.2019.mar.17.

    Article  Google Scholar 

  • Díaz-Faes, A. A., Bowman, T. D., & Costas, R. (2019). Towards a second generation of ‘social media metrics’: Characterizing Twitter communities of attention around science. PLoS ONE, 14(5), e0216408. https://doi.org/10.1371/journal.pone.0216408.

    Article  Google Scholar 

  • Didegah, F., Mejlgaard, N., & Sørensen, M. P. (2018). Investigating the quality of interactions and public engagement around scientific papers on twitter. Journal of Informetrics, 12(3), 960–971. https://doi.org/10.1016/J.JOI.2018.08.002.

    Article  Google Scholar 

  • DiResta, R., & Lotan, G. (2015). Anti-vaxxers are using Twitter to manipulate a vaccine bill. Wired. Retrieved from https://www.wired.com/2015/06/antivaxxers-influencing-legislation/.

  • Dubois, G., & Blank, G. (2018). The echo chamber is overstated: The moderating effect of political interest and diverse media. Information, Communication & Sociey, 21(5), 729–745. https://doi.org/10.1080/1369118X.2018.1428656.

    Article  Google Scholar 

  • Enkhbayar, A., Haustein, S., Barata, G., & Alperin, J. P. (2019). How much research shared on Facebook is hidden from public view? A comparison of public and private online activity around PLOS ONE papers. ArXiv. http://arxiv.org/abs/1909.01476.

  • Feng, Y. (2016). Are you connected? Evaluating information cascades in online discussion about the #RaceTogether campaign. Computers in Human Behavior, 54, 43–53. https://doi.org/10.1016/j.chb.2015.07.052.

    Article  Google Scholar 

  • Frandsen, F., & Johansen, W. (2015). Organizations, stakeholders, and intermediaries: Towards a general theory. International Journal of Strategic Communication, 9(4), 253–271. https://doi.org/10.1080/1553118X.2015.1064125.

    Article  Google Scholar 

  • Gerbaudo, P. (2012). Tweets and the streets: Social media and contemporary activism. London: Pluto.

    Google Scholar 

  • Guenther, L. (2019). Science journalism. In H. Ornebring (Ed.), Oxford encyclopedia of journalism. New York: Oxford University Press.

    Google Scholar 

  • Hansen, D., Shneiderman, B., & Smith, M. (2010). Analyzing social media networks with NodeXL: Insights from a connected world. Burlington MA: Morgan Kaufman.

    Google Scholar 

  • Harel, D. H., & Kohen, Y. (2001). A fast multi-scale method for drawing large graphs [Post-print]. Retrieved from http://www.wisdom.weizmann.ac.il/~harel/papers/ms_jgaa.pdf.

  • Haustein, S. (2016). Grand challenges in altmetrics: Heterogeneity, data quality and dependencies. Scientometrics, 108, 413–423. https://doi.org/10.1007/s11192-016-1910-9.

    Article  Google Scholar 

  • Haustein, S., Bowman, T. D., & Costas, R. (2015). Communities of attention around journal papers: Who is tweeting about scientific publications. Social Media and Society 2015 International Conference. pp. 1–21. Toronto Retrieved from https://es.slideshare.net/StefanieHaustein/communities-of-attention-around-journal-papers-who-is-tweeting-about-scientific-publications.

  • Haustein, S., Bowman, T., & Costas, R. (2016). Interpreting ‘altmetrics’: Viewing acts on social media through the lens of citation and social theories. In C. Sugimoto (Ed.), Theories of Infometrics and Scholarly Communication (pp. 372–406). Berlin: De Gruyter. https://doi.org/10.1515/9783110308464-022.

  • Haustein, S. D., Bowman, T., Holmberg, K., Peters, I., & Larivière, V. (2014). Astrophysicists on Twitter: An in-depth analysis of tweeting and scientific publication behavior. Aslib Journal of Information Management, 66, 279–296.

    Article  Google Scholar 

  • Haustein, S., & Costas, R. (2015). Identifying Twitter audiences: Who is tweeting about scientific papers? ASIS&T SIG/MET Metrics 2015 Workshop (pp. 1–3).

  • Haustein, S., Costas, R., & Lariviere, V. (2015b). Characterising social media metrics of scholarly papers: The effect of document properties and collaboration patterns. PLoS ONE, 10(5), e0127830. https://doi.org/10.1371/journal.pone.0120495.

    Article  Google Scholar 

  • Himelboim, I., Smith, M., Raine, L., Shneiderman, B., & Espina, C. (2017). Classifying Twitter topic-networks using social network analysis. Social Media + Society. https://doi.org/10.1177/20563051176915.

    Article  Google Scholar 

  • Jasanoff, S. (2006). Transparency in public science: Purposes, reasons, limits. Law and Contemporary Problems, 69(21), 21–45.

    Google Scholar 

  • Kahan, D. (2013). A risky science communication environment for vaccines. Science, 342, 53–54.

    Article  Google Scholar 

  • Kahan, D. (2014). Vaccine risk perceptions and ad hoc risk communication: An empirical assessment. New Haven: Cultural Cognition Project, Yale Law School.

    Google Scholar 

  • Kata, A. (2012). Anti-vaccine activists, Web 2.0, and the postmodern paradigm: An overview of the tactics and tropes used online by the anti-vaccination movement. Vaccine, 30, 3778–3789.

    Article  Google Scholar 

  • Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14, 10–25. https://doi.org/10.1002/asi.5090140103.

    Article  Google Scholar 

  • Kumar, S., Hamilton, W., Leskovec, J., & Jurafsky, D. (2018). Community interaction and conflict on the web. WWW 2018: The 2018 Web Conference, April 23–27, 2018, Lyon, France (pp. 1–11). New York: ACM.

  • Landrum, A. (2017). The role of intermediaries in communicating science: A synthesis. In K. Jamieson, D. Kahan, & D. Scheufele (Eds.), The Oxford handbook of the science of science communication (pp. 253–260). New York: Oxford University Press.

    Google Scholar 

  • Larson, H., Figueiredo, A., Xiahong, Z., Schulz, W., Verger, P., Johnston, D., et al. (2016). The state of vaccine confidence 2016: Global insights through a 67-country survey. EBioMedicine, 12, 295–301. https://doi.org/10.1016/j.ebiom.2016.08.042.

    Article  Google Scholar 

  • Leask, J. (2015). Should we do battle with anti-vaccination activists? Public Health Research & Practice, 25(2), e2521515. https://doi.org/10.17061/phrp2521515.

    Article  Google Scholar 

  • Leonelli, S., Spichtinger, D., & Prainsack, B. (2015). Sticks and carrots: Encouraging open science at its source. Geography and Environment, 2, 12–16. https://doi.org/10.1002/geo2.2.

    Article  Google Scholar 

  • Lo, N., & Hotez, P. (2017). Public health and economic consequences of vaccine hesitancy for measles in the United States. JAMA Pediatrics, 171(9), 887–892. https://doi.org/10.1001/jamapediatrics.2017.1695.

    Article  Google Scholar 

  • Miller, V. (2017). Phatic culture and the status quo: Reconsidering the purpose of social media activism. Convergence, 23(3), 251–269. https://doi.org/10.1177/1354856515592512.

    Article  Google Scholar 

  • Mitra, T., Counts, S., & Pennebaker, J. (2016). Understanding anti-vaccination attitudes in social media. In Proceedings of the tenth international AAAI conference on web and social media (ICWSM 2016) (pp. 269–278). Palo Alto: The AAAI Press.

  • Mohammadi, E., Thelwall, M., Kwasny, M., & Holmes, K. L. (2018). Academic information on Twitter: A user survey. PLoS ONE, 13(5), e0197265. https://doi.org/10.1371/journal.pone.0197265.

    Article  Google Scholar 

  • Mohammadi, E., Barahmand, N., & Thelwall, M. (2019). Who shares health and medical scholarly articles on Facebook? Learned Publishing. https://doi.org/10.1002/leap.1271.

    Article  Google Scholar 

  • Monge, P., & Contractor, N. (2003). Theories of communication networks. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Moran, B., Lucas, M., Everhart, K., & Morgan, A. (2016). What makes anti-vaccine websites persuasive? A content analysis of techniques used by antivaccine vaccine sentiment. Journal of Communication in Healthcare, 9(3), 151–163. https://doi.org/10.1080/17538068.2016.1235531.

    Article  Google Scholar 

  • Moten, M., Phillips, M., Saliba, V., Harding, N., & Sibal, B. (2018). Measles: Is it still a threat? British Journal of General Practice. https://doi.org/10.3399/bjgp18X697961.

    Article  Google Scholar 

  • Muller, J. (2017). Universities and the ‘new society’. In J. Muller, N. Cloete, & F. Van Schalkwyk (Eds.), Castells in Africa: Universities and development (pp. 17–31). Cape Town: African Minds.

    Google Scholar 

  • Nelhans, G., & Lorentzen, D. G. (2016). Twitter conversation patterns related to research papers. Information Research, 21(2). Retrieved from http://www.informationr.net/ir/21-2/SM2.html.

  • Piwowar, H., Priem, J., Larivière, V., Alperin, J., Matthias, L., Norlander, B., et al. (2017). The state of OA: A large-scale analysis of the prevalence and impact of open access articles. PeerJ Preprints 5, e3119v1. https://doi.org/10.7287/peerj.preprints.3119v1.

  • Priem, J., Taraborelli, D., Groth, P., & Neylon, C. (2011). Altmetrics: A manifesto. Altmetrics. Retrieved from http://altmetrics.org/manifesto/.

  • Puschmann, C. (2014). (Micro)Blogging science? Notes on potentials and constraints of new forms of scholarly communication. In S. Bartling & S. Friesike (Eds.), Opening science. Cham: Springer.

    Google Scholar 

  • Robinson-Garcia, N., Costas, R., Isett, K., Melkers, J., & Hicks, D. (2017). The unbearable emptiness of tweeting about journal articles. PLoS ONE, 12(8), e0183551. https://doi.org/10.1371/journal.pone.0183551.

    Article  Google Scholar 

  • Robinson-Garcia, N., Van Leeuwen, T. N., & Ràfols, I. (2018). Using altmetrics for contextualised mapping of societal impact: From hits to networks. Science and Public Policy, 45(6), 815–826.

    Article  Google Scholar 

  • Rogers, R. (2015). Digital methods for web research. In R. Scott & S. Kosslyn (Eds.), Emerging trends in the social and behavioural sciences (pp. 1–22). New York: Wiley.

    Google Scholar 

  • Roosendaal, H., & Geurts, P. (1997). Forces and functions in scientific communication: An analysis of their interplay. Cooperative Research Information Systems in Physics, 31 August to 4 September 1997, Oldenburg, Germany. Retrieved from http://www.physik.uni-oldenburg.de/conferences/crisp97/roosendaal.html.

  • Sanawi, J., Samani, M., & Taibi, M. (2017). #Vaccination: Identifying influencers in the vaccination discussion on Twitter through social network visualisation. International Journal of Business and Society, 18(S4), 718–726.

    Google Scholar 

  • Schäfer, M. (2017). How changing media structures are affecting science news coverage. In K. Jamieson, D. Kahan, & D. Scheufele (Eds.), The Oxford handbook of the science of science communication (pp. 51–59). New York: Oxford University Press.

    Google Scholar 

  • Scheufele, D. (2014). Science communication as political communication. PNAS, 111(4), 13585–13592.

    Article  Google Scholar 

  • Smith, M. (2018). How large a network can NodeXL handle? Social media research foundation [Web page]. https://www.smrfoundation.org/faq/how-large-a-network-can-nodexl-handle/.

  • Southwell, B. (2017). Promoting popular understanding of science and health through social networks. In K. Jamieson, D. Kahan, & D. Scheufele (Eds.), The Oxford handbook of the science of science communication (pp. 223–231). New York: Oxford University Press.

    Google Scholar 

  • Stalder, F. (2006). Manuel castells: The theory of the network society. Cambridge: Polity.

    Google Scholar 

  • Statista. (2019a). Number of monthly active Twitter users worldwide from 1st quarter 2010 to 1st quarter 2019 (in millions). Retrieved from https://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users/.

  • Statista. (2019b). Number of monthly active Facebook users worldwide as of 4th quarter 2019 (in millions). Retrieved from https://www.statista.com/statistics/264810/number-of-monthly-active-facebook-users-worldwide/.

  • Sugimoto, C., Work, S., Larivière, V., & Haustein, S. (2017). Scholarly use of social media and altmetrics: A review of the literature. Journal of the Association for Information Science and Technology, 68(9), 2037–2062. https://doi.org/10.1002/asi.23833.

    Article  Google Scholar 

  • Thelwall, M., Tsou, A., Weingart, S., Holmberg, K., & Haustein, S. (2013). Tweeting links to academic articles. Cybermetrics: International Journal of Scientometrics, Informetrics and Bibliometrics, 17, 1–8.

    Google Scholar 

  • Tsou, A., Bowman, T., Ghazinejad, A., & Sugimoto, C. (2015). Who tweets about science? In Proceedings of the 2015 international society for scientometrics and informetrics (pp. 95–100). Istanbul, Turkey. https://pdfs.semanticscholar.org/81fe/8b63188cf25648a7c592bc6b5457fee3c101.pdf.

  • Tucker, J., Guess, A., Barberá, P., Vaccari, C., Siegel, A., Sanovich, S., et al. (2018). Social media, political polarization, and political disinformation: A review of the scientific literature. Washington DC: William and Flora Hewlett Foundation.

    Google Scholar 

  • Vainio, J., & Holmberg, K. (2017). Highly tweeted science articles: Who tweets them? An analysis of Twitter user profile descriptions. Scientometrics, 112, 345–366. https://doi.org/10.1007/s11192-017-2368-0.

    Article  Google Scholar 

  • Van Dijck, J., & Poell, T. (2013). Understanding social media logic. Media and Communication, 1(1), 2–14.

    Article  Google Scholar 

  • Van Schalkwyk, F. (2018). List of journal articles on vaccination and autism [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3758053.

  • Van Schalkwyk, F. (2019a). New potentials in the communication of open science with non‐scientific publics: The case of the anti‐vaccination movement (Doctoral dissertation. Stellenbosch University). https://doi.org/10.13140/rg.2.2.28501.06888.

  • Van Schalkwyk, F. (2019b). The amplification of uncertainty: The use of science in the social media by the anti-vaccination movement. In P. Weingart, M. Joubert, & B. Falade (Eds.), Science communication in South Africa: Reflections on current issues (pp. 170–212). Cape Town: Africa Minds.

    Google Scholar 

  • Vanderslott, S., & Roser, M. (2018). Vaccination. Our world in data. Retrieved from https://ourworldindata.org/vaccination.

  • Venturini, T. (2012). Building on faults: How to represent controversies with digital methods. Public Understanding of Science, 21(7), 796–812.

    Article  Google Scholar 

  • Weingart, P. (2011). Science, the public and the media—Views from everywhere. In M. Carrier & A. Nordmann (Eds.), Science in the context of application (pp. 337–348). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Weingart, P., & Guenther, L. (2016). Science communication and the issue of trust. JCOM, 15(5), 1–7.

    Article  Google Scholar 

  • WHO (World Health Organization). (2019). Ten threats to global health in 2019. WHO website. https://www.who.int/emergencies/ten-threats-to-global-health-in-2019.

  • Williams, J. (2018). Stand out of my light: Freedom and resistance in the attention economy. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Wu, T. (2016). The attention merchants: The epic struggle to get inside our heads. London: Atlantic.

    Google Scholar 

  • Yeo, S., Xenos, M., Brossard, D., & Scheufele, D. (2015). Selecting our own science: How communication contexts and individual traits shape information seeking. The ANNALS of the American Academy of Political and Social Science, 658(1), 172–191. https://doi.org/10.1177/0002716214557782.

    Article  Google Scholar 

  • Yuan, X., Schuchard, R. J., & Crooks, A. T. (2019). Examining emergent communities and social bots within the polarized online vaccination debate in Twitter. Social Media + Society. https://doi.org/10.1177/2056305119865465.

    Article  Google Scholar 

  • Zahedi, Z., & Costas, R. (2018). General discussion of data quality challenges in social media metrics: Extensive comparison of four major altmetric data aggregators. PLoS ONE, 13(5), e0197326. https://doi.org/10.1371/journal.pone.0197326.

    Article  Google Scholar 

  • Zannettou, S., Caulfield, T., De Cristofaro, E., Kourtellis, N., Leontiadis, I., Sirivianos, M., et al. (2017). The web centipede: Understanding how web communities influence each other through the lens of mainstream and alternative news sources. IMC17, November 1–3, 2017, London, UK. New York: ACM. https://doi.org/10.1145/3131365.3131390.

  • Zhao, D., & Strotmann, A. (2008). Evolution of research activities and intellectual influences in information science 1996–2005: Introducing author bibliographic-coupling analysis. Journal of American Society for Information Science and Technology, 59, 2070–2086. https://doi.org/10.1002/asi.20910.

    Article  Google Scholar 

  • Zhou, Y., & Na, J. (2019). A comparative analysis of Twitter users who Tweeted on psychology and political science journal articles. Online Information Review. https://doi.org/10.1108/OIR-03-2019-0097.

    Article  Google Scholar 

  • Zimmerman, R., Wolfe, R., & Fox, D. (2005). Vaccine criticism on the world wide web. Journal of Medical Internet Research, 7(2), e17.

    Article  Google Scholar 

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Acknowledgements

The authors thank the reviewers of this and previous versions of this paper for their comments and constructive suggestions. The authors thank Altmetric.com for providing access to its Twitter data. This work is based on research supported by the South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation (NRF) of South Africa (Grant No. 93097). Any opinion, finding and conclusion or recommendation expressed in this material is that of the author(s) and the NRF does not accept any liability in this regard. Rodrigo Costas is partially funded by the South African DST-NRF Centre of Excellence in Scientometrics and Science, Technology and Innovation Policy (SciSTIP) (Grant No. 91488).

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  1. South African Research Chair in Science Communication, Centre for Research on Evaluation, Science and Technology (CREST), Stellenbosch University, Private Bag X1 Matieland, Stellenbosch, 7602, South Africa

    Francois van Schalkwyk

  2. Centre for Science and Technology Studies (CWTS), Leiden University, Wassenaarseweg 62A, 2333 AL, Leiden, The Netherlands

    Jonathan Dudek & Rodrigo Costas

  3. DST-NRF Centre of Excellence in Scientometrics and Science, Technology and Innovation Policy (SciSTIP), Centre for Research on Evaluation, Science and Technology (CREST), Stellenbosch University, Private Bag X1 Matieland, Stellenbosch, 7602, South Africa

    Rodrigo Costas

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Correspondence to Francois van Schalkwyk.

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van Schalkwyk, F., Dudek, J. & Costas, R. Communities of shared interests and cognitive bridges: the case of the anti-vaccination movement on Twitter. Scientometrics 125, 1499–1516 (2020). https://doi.org/10.1007/s11192-020-03551-0

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