Literature DB >> 29773322

Polarization of the vaccination debate on Facebook.

Ana Lucía Schmidt1, Fabiana Zollo2, Antonio Scala3, Cornelia Betsch4, Walter Quattrociocchi5.   

Abstract

BACKGROUND: Vaccine hesitancy has been recognized as a major global health threat. Having access to any type of information in social media has been suggested as a potential influence on the growth of anti-vaccination groups. Recent studies w.r.t. other topics than vaccination show that access to a wide amount of content through the Internet without intermediaries resolved into major segregation of the users in polarized groups. Users select information adhering to theirs system of beliefs and tend to ignore dissenting information.
OBJECTIVES: The goal was to assess whether users' attitudes are polarized on the topic of vaccination on Facebook and how this polarization develops over time.
METHODS: We perform a thorough quantitative analysis by studying the interaction of 2.6 M users with 298,018 Facebook posts over a time span of seven years and 5 months. We applied community detection algorithms to automatically detect the emergence of communities accounting for the users' activity on the pages. Also, we quantified the cohesiveness of these communities over time.
RESULTS: Our findings show that the consumption of content about vaccines is dominated by the echo chamber effect and that polarization increased over the years. Well-segregated communities emerge from the users' consumption habits i.e., the majority of users consume information in favor or against vaccines, not both.
CONCLUSION: The existence of echo chambers may explain why social-media campaigns that provide accurate information have limited reach and be effective only in sub-groups, even fomenting further opinion polarization. The introduction of dissenting information into a sub-group is disregarded and can produce a backfire effect, thus reinforcing the pre-existing opinions within the sub-group. Public health professionals should try to understand the contents of these echo chambers, for example by getting passively involved in such groups. Only then it will be possible to find effective ways of countering anti-vaccination thinking.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Keywords:  Anti-vaccine sentiment; Computational social science; Misinformation; Network analysis; Social media

Mesh:

Year:  2018        PMID: 29773322     DOI: 10.1016/j.vaccine.2018.05.040

Source DB:  PubMed          Journal:  Vaccine        ISSN: 0264-410X            Impact factor:   3.641


  56 in total

1.  [Vaccination ethics-a sketch of moral challenges and ethical criteria].

Authors:  Peter Schröder-Bäck; Kyriakos Martakis
Journal:  Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz       Date:  2019-04       Impact factor: 1.513

Review 2.  Understanding the use of digital technology to promote human papillomavirus vaccination - A RE-AIM framework approach.

Authors:  Ashley B Stephens; Chelsea S Wynn; Melissa S Stockwell
Journal:  Hum Vaccin Immunother       Date:  2019-06-18       Impact factor: 3.452

Review 3.  Social media and vaccine hesitancy: new updates for the era of COVID-19 and globalized infectious diseases.

Authors:  Neha Puri; Eric A Coomes; Hourmazd Haghbayan; Keith Gunaratne
Journal:  Hum Vaccin Immunother       Date:  2020-07-21       Impact factor: 3.452

4.  Facebook HPV vaccine campaign: insights from Brazil.

Authors:  Cássia Rita Pereira da Veiga; Elder Semprebon; Jacqueline Laurindo da Silva; Vinicius Lins Ferreira; Claudimar Pereira da Veiga
Journal:  Hum Vaccin Immunother       Date:  2020-01-09       Impact factor: 3.452

5.  Vaccine-related advertising in the Facebook Ad Archive.

Authors:  Amelia M Jamison; David A Broniatowski; Mark Dredze; Zach Wood-Doughty; DureAden Khan; Sandra Crouse Quinn
Journal:  Vaccine       Date:  2019-11-13       Impact factor: 3.641

6.  Influencing dynamics on social networks without knowledge of network microstructure.

Authors:  Matthew Garrod; Nick S Jones
Journal:  J R Soc Interface       Date:  2021-08-25       Impact factor: 4.293

7.  #Scamdemic, #Plandemic, or #Scaredemic: What Parler Social Media Platform Tells Us about COVID-19 Vaccine.

Authors:  Annalise Baines; Muhammad Ittefaq; Mauryne Abwao
Journal:  Vaccines (Basel)       Date:  2021-04-22

8.  Future directions of the National Institutes of Health Science of Behavior Change Program.

Authors:  Chandra Keller; Rebecca A Ferrer; Rosalind B King; Elaine Collier
Journal:  Transl Behav Med       Date:  2021-04-10       Impact factor: 3.046

9.  Using Machine Learning to Compare Provaccine and Antivaccine Discourse Among the Public on Social Media: Algorithm Development Study.

Authors:  Young Anna Argyris; Kafui Monu; Pang-Ning Tan; Colton Aarts; Fan Jiang; Kaleigh Anne Wiseley
Journal:  JMIR Public Health Surveill       Date:  2021-06-24

10.  Analysing Twitter semantic networks: the case of 2018 Italian elections.

Authors:  Tommaso Radicioni; Fabio Saracco; Elena Pavan; Tiziano Squartini
Journal:  Sci Rep       Date:  2021-06-24       Impact factor: 4.379

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