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

Public (e)Health, Digital Epidemiology and Public Health Informatics (1142) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1994) Infodemiology and Infoveillance (1613) Natural Language Processing (988) Pharmacovigilance (185) Theme Issue: Medical Informatics and COVID-19 (126)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/63755, first published .
Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis

Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis

Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis

Authors of this article:

Wanxin Li1 Author Orcid Image ;   Yining Hua2, 3 Author Orcid Image ;   Peilin Zhou4 Author Orcid Image ;   Li Zhou3 Author Orcid Image ;   Xin Xu1 Author Orcid Image ;   Jie Yang1, 5 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

Journals

  1. Ried P, Seifert R. Social media data and its potential for pharmacovigilance: a comparative analysis of reported prevalences regarding drug-induced gingival overgrowth (DIGO). Naunyn-Schmiedeberg's Archives of Pharmacology 2026;399(7):9673 View
  2. Aljaafari M, Sorour S. GenAI Agent for Automated Analysis and Personalization of Drug Prevention Campaigns. Scientific Journal of King Faisal University Humanities and Management Sciences 2026:68 View
  3. Guellil I, Berrachedi Y, Chenni N, Abboud M, Wu J, Wu H, Alex B. Detecting Adverse Drug Events in Social Media: A Brief Literature Review. SN Computer Science 2026;7(2) View
  4. 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