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

Infodemic Management (54) JMIR Theme Issue: COVID-19 Special Issue (2493) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1087) Infodemiology and Infoveillance (1454) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1530) Vaccination and Immunization in the Digital Age (401) Machine Learning (1730) Discourse (39)

Published on in Vol 2, No 1 (2022): Jan-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34231, first published .
The Role of Influential Actors in Fostering the Polarized COVID-19 Vaccine Discourse on Twitter: Mixed Methods of Machine Learning and Inductive Coding

The Role of Influential Actors in Fostering the Polarized COVID-19 Vaccine Discourse on Twitter: Mixed Methods of Machine Learning and Inductive Coding

The Role of Influential Actors in Fostering the Polarized COVID-19 Vaccine Discourse on Twitter: Mixed Methods of Machine Learning and Inductive Coding

Authors of this article:

Loni Hagen1 Author Orcid Image ;   Ashley Fox2 Author Orcid Image ;   Heather O'Leary3 Author Orcid Image ;   DeAndre Dyson1 Author Orcid Image ;   Kimberly Walker4 Author Orcid Image ;   Cecile A Lengacher5 Author Orcid Image ;   Raquel Hernandez6 Author Orcid Image
Article Authors Cited by (11) Tweetations (16) Metrics

Journals

  1. Zang S, Zhang X, Xing Y, Chen J, Lin L, Hou Z. Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review. Journal of Medical Internet Research 2023;25:e40057 View
  2. Van Bavel J, Pretus C, Rathje S, Pärnamets P, Vlasceanu M, Knowles E. The Costs of Polarizing a Pandemic: Antecedents, Consequences, and Lessons. Perspectives on Psychological Science 2024;19(4):624 View
  3. Isip Tan I, Cleofas J, Solano G, Pillejera J, Catapang J. Interdisciplinary Approach to Identify and Characterize COVID-19 Misinformation on Twitter: Mixed Methods Study. JMIR Formative Research 2023;7:e41134 View
  4. Ng Q, Lee D, Ng C, Yau C, Lim Y, Liew T. Examining the Negative Sentiments Related to Influenza Vaccination from 2017 to 2022: An Unsupervised Deep Learning Analysis of 261,613 Twitter Posts. Vaccines 2023;11(6):1018 View
  5. Harris M, Murtfeldt R, Wang S, Mordecai E, West J, Ognyanova K. Perceived experts are prevalent and influential within an antivaccine community on Twitter. PNAS Nexus 2024;3(2) View
  6. Nguyen K, McChesney C, Rodriguez C, Vasudevan L, Bednarczyk R, Corlin L. Child and adolescent COVID-19 vaccination coverage by educational setting, United States. Public Health 2024;229:126 View
  7. Wang H, van Prooijen J, van Lange P. How perceived coercion polarizes unvaccinated people: The mediating role of conspiracy beliefs. Journal of Health Psychology 2024 View
  8. Florent N. Bridging the gap: Political engagement and trust as mediators of racial health inequities and vaccine reluctance. Advances in Health and Behavior 2024;7(1):281 View
  9. Dinh L, Hong L, Dumas C, Patin B, Ghosh S, Li L, Khoury C. Social Media and Crisis Informatics Research in LIS. Proceedings of the Association for Information Science and Technology 2024;61(1):749 View
  10. Li Z, Zhou Q, Zhang R. The Persuasion Effects of Vaccination Message: The Interactive Effects of Language Style, Source Type, and the Provision of Choice. Science Communication 2025 View
  11. Hartmann D, Wang S, Pohlmann L, Berendt B. A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes. Journal of Computational Social Science 2025;8(2) View