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

Infodemic Management (38) JMIR Theme Issue: COVID-19 Special Issue (2275) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (946) Infodemiology and Infoveillance (1166) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1402) Vaccination and Immunization in the Digital Age (304) Machine Learning (1189)

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 Hagen 1 Author Orcid Image ;   Ashley Fox 2 Author Orcid Image ;   Heather O'Leary 3 Author Orcid Image ;   DeAndre Dyson 1 Author Orcid Image ;   Kimberly Walker 4 Author Orcid Image ;   Cecile A Lengacher 5 Author Orcid Image ;   Raquel Hernandez 6 Author Orcid Image
Article Authors Cited by (4) Tweetations (17) 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 2023 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