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Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57395, first published .
Opioid crisis data analysis on a computer screen in a medical setting.

Public Health Discussions on Social Media: Evaluating Automated Sentiment Analysis Methods

Public Health Discussions on Social Media: Evaluating Automated Sentiment Analysis Methods

Journals

  1. Bizri-Baryak R, Ivanitskaya L, Erzikova E, Kreps G. Analyzing the Overturn of Roe v. Wade: A Term Co-Occurrence Network Analysis of YouTube Comments. Informatics 2025;12(2):49 View
  2. Ahmad M, Batyrshin I, Sidorov G. Sentiment Analysis Using a Large Language Model–Based Approach to Detect Opioids Mixed With Other Substances Via Social Media: Method Development and Validation. JMIR Infodemiology 2025;5:e70525 View
  3. Yuan H, Ke R, Xie X. Sentiment analysis of visitor perceptions on architectural heritage: a case study of Phoenix Ancient Town for sustainable conservation and development. Journal of Asian Architecture and Building Engineering 2025:1 View
  4. Torwane N, Lalloo R, Ha D, Do L. Analysis of Water Fluoridation Debates on Meta Platforms Using Advanced Machine Learning Approaches. JDR Clinical & Translational Research 2025 View
  5. Stadelmann D, Thomas T, Zakharov N. Too hot to play it cool? Temperature and negative media bias. Public Choice 2026;206(3-4):593 View
  6. Herzog A, Zuo M, Patel R, Canete J, Lee E. Hashtags and healing: Leveraging reddit for holistic ostomy care. The American Journal of Surgery 2026;252:116722 View
  7. Li Y, Chen R, Yang X, Liu X, Zhu G, Hu Q. A Quantitative Sustainability Assessment Framework for Contaminated Site Remediation: Integrating LCA, Economic Analysis, and Social Big Data. Water 2025;17(23):3416 View
  8. Ahmad M, Orji R, Amjad M, Siddique A, Kubysheva N, Batyrshin I, Sidorov G. Automated Risk Assessment of Opioid Use: Analysis Using Pre-Trained Transformers on Social Media Data. JMIR Infodemiology 2026;6:e77783 View
  9. Nazir M, Bilal M, Shongwe S. Sentiment analysis for code-mixed low-resource languages: a systematic review of approaches, techniques, applications, challenges, and future directions. Social Network Analysis and Mining 2026;16(1) View
  10. Fundisi E, Dlamini Q. Ten Years on: A Revisit on the #FeesMustFall Movement Discourse on Twitter/X. Journalism and Media 2026;7(2):98 View
  11. Mohammed A, Ovalle-Eliseo S, Mohammed J, Islas Huerta G, Monserratt L, Andrade D, Garcia J, Kaufman R, Gutierrez M, Díaz-Santos M. Methodological Framework for the Design and Implementation of a US Latine-Hispanic Digital Brain Health Program: User-Centered Design Approach. JMIR Formative Research 2026;10:e73445 View
  12. Marciniak M, Setzen S, Bhattacharya D, Masliah J, Powszok R, Sturgis E, Stubbs V, Bhayani M. Human Papillomavirus Vaccine Discourse and Sentiment on Reddit Before and After COVID-19: Mixed Methods Retrospective Cross-Sectional Study. Journal of Medical Internet Research 2026;28:e83558 View
  13. Azhar A, Arief Dwi Saputra , Alfina Rahmatia . Sadvertising and Sentiment: A Lexicon-Based YouTube Comment Analysis of Emotionally Resonant Thai Insurance Advertising. Journal of Digital Marketing and Communication 2026;6(1) View
  14. Cruz M, Hernández S, Sánchez J, Medina R, Gómez A, Orozco A. Evaluating Pre-Trained Transformer-Based Models for Political Sentiment Analysis on Social Media. Computation 2026;14(6):127 View

Books/Policy Documents

  1. Procter R. The Cambridge Handbook of Behavioural Data Science. View
  2. . The Cambridge Handbook of Behavioural Data Science. View

Conference Proceedings

  1. Vaz N, Silva K, Velasco G, Mata L, Fernandes D, Carvalho S. Anais do XXV Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2025). Towards a Personalized mHealth Model Using Intelligent Conversational Agents View