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

Peer-to-Peer Support and Online Communities (1013) Infodemiology and Infoveillance (2043) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1425) JMIR Theme Issue: COVID-19 Special Issue (4239) Infoveillance and Social Listening (441)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67968, first published .
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Exploring Conceptualizations of COVID-19 Risk in Ideologically Distinct Online Communities: A Computational Grounded Theory Analysis

Exploring Conceptualizations of COVID-19 Risk in Ideologically Distinct Online Communities: A Computational Grounded Theory Analysis

Authors of this article:

Tiwaladeoluwa B Adekunle1 Author Orcid Image ;   Jeremy Foote2 Author Orcid Image ;   Toluwani E Adekunle3 Author Orcid Image ;   Nathan TeBlunthuis4 Author Orcid Image ;   Laura K Nelson5 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

Journals

  1. Shankar R, Devi F, Qian X. A systematic review of the application of computational grounded theory method in healthcare research. Biology Methods and Protocols 2025;10(1) View
  2. Ye X, Zhao Y, Wang D, Zhang Y. When the Magic Fades: Understanding Determinants of User Disengagement with AI Companions. International Journal of Human–Computer Interaction 2026:1 View
  3. Bong K, Pangestu J, Gunawan A. Developer Discourse on Generative AI Programming Tools on Reddit: A Computational Grounded Theory Analysis. Procedia Computer Science 2026;289:629 View
  4. Shu W, Deng L, Liu P, Deng J, Ye X. A conceptual framework for pragmatic acceptance of healthcare robots among older adults: a computational grounded theory. BMC Medical Ethics 2026;27(1) View