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

Infodemiology and Infoveillance (1570) Peer-to-Peer Support and Online Communities (814) Pharmacovigilance (174) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1146) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1932) Substance Abuse (540) Public (e)Health, Digital Epidemiology and Public Health Informatics (1032)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57885, first published .
Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis

Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis

Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis

Authors of this article:

Varun K Rao1 Author Orcid Image ;   Danny Valdez2 Author Orcid Image ;   Rasika Muralidharan3 Author Orcid Image ;   Jon Agley2 Author Orcid Image ;   Kate S Eddens1 Author Orcid Image ;   Aravind Dendukuri3 Author Orcid Image ;   Vandana Panth3 Author Orcid Image ;   Maria A Parker2 Author Orcid Image
Article Authors Cited by (4) Tweetations (1) Metrics

Journals

  1. Phillips J. “Get This Deadly Drug off Our Streets, and End This Crisis”: Discussing Fentanyl (in the US) on Facebook and Examining the Agenda Setting Factors that Drive This Online Content. Journal of Drug Issues 2025 View
  2. Muralidharan R, Soto-Vasquez A, Montenegro M, Valdez D. Analysis of Breast Cancer Information on Facebook Using Neural Network–Based Topic Modeling and Metadata Analysis of English and Spanish Content: Comparative Study. Journal of Medical Internet Research 2025;27:e79161 View
  3. Altarawneh A, Arzoky M, Swift S, Al Fayez R, Al-Zoubi M, Sowan B, Zhang L. ETM-F: an enriched topic modeling and filtration framework integrating ontologies and deep learning for biomedical trend analysis. Knowledge and Information Systems 2026;68(1) View

Books/Policy Documents

  1. Prieto-Santamaría L, Iglesias A, Giné C, Calderón F, Lozano Ó, Rodríguez-González A. Artificial Intelligence in Biomedicine. View