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

Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1933) Opioid and Related Substance Misuse (205) Infodemiology and Infoveillance (1570) Infoveillance and Social Listening (317) Machine Learning (2134) Public (e)Health, Digital Epidemiology and Public Health Informatics (1032) Substance Abuse (540)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67506, first published .
Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis

Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis

Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis

Authors of this article:

ShinYe Kim1 Author Orcid Image ;   Winson Fu Zun Yang2 Author Orcid Image ;   Zishan Jiwani1 Author Orcid Image ;   Emily Hamm1 Author Orcid Image ;   Shreya Singh1 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Hapizah S, Ernawati E, Hermon D. The Impact of the REACT Learning Model on Students Activity and Academic Performance. Jurnal Penelitian Pendidikan IPA 2025;11(12):570 View