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

Infodemiology and Infoveillance (1604) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1977) Public (e)Health, Digital Epidemiology and Public Health Informatics (1121) Natural Language Processing (967) HIV/AIDS/STI Prevention and Care (1091) Machine Learning (2253) Infoveillance and Social Listening (325) Behavioral Surveillance for Population and Public Health Informatics (61) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (105)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/76745, first published .
Decoding HIV Discourse on Social Media: Large-Scale Analysis of 191,972 Tweets Using Machine Learning, Topic Modeling, and Temporal Analysis

Decoding HIV Discourse on Social Media: Large-Scale Analysis of 191,972 Tweets Using Machine Learning, Topic Modeling, and Temporal Analysis

Decoding HIV Discourse on Social Media: Large-Scale Analysis of 191,972 Tweets Using Machine Learning, Topic Modeling, and Temporal Analysis

Authors of this article:

Xiangming Zhan1 Author Orcid Image ;   Meijia Song2 Author Orcid Image ;   Cho Hee Shrader3 Author Orcid Image ;   Chad E Forbes1 Author Orcid Image ;   Angel B Algarin1 Author Orcid Image
Article Authors Cited by (3) Tweetations (1) Metrics

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  2. Youm S, Jang S, Kwak H, Choi J, Yi Y. Cross-Cultural Differences in Public Discourse on COVID-19 Vaccination in the United States and South Korea: Cross-Sectional Analysis Using Natural Language Processing. JMIR Formative Research 2026;10:e84791 View
  3. Li J, Fu J, Sun Q, Lei H, Li J, Li Q, Wang Y, Hu J, Yan X, Regencia Z. Assessing the reliability and quality of content on social media regarding biologic therapies for psoriasis: A cross-sectional content analysis. PLOS One 2026;21(4):e0345570 View