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

Public (e)Health, Digital Epidemiology and Public Health Informatics (1061) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1945) Infodemiology and Infoveillance (1583) Natural Language Processing (938) Theme Issue: Medical Informatics and COVID-19 (125) Pharmacovigilance (178)

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 (3) 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 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