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

Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (2613) Infodemiology and Infoveillance (1952) Public (e)Health, Digital Epidemiology and Public Health Informatics (2203) Substance Abuse (642) Machine Learning (3006) Opioid and Related Substance Misuse (232) Infoveillance and Social Listening (390)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67506, first published .
Sick woman in bed on phone surrounded by oversized pills, telemedicine concept

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 (2) 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
  2. Valades T, Ruiz-Iniesta A, Domingo-Espiñeira J, Chart-Pascual J, Fraile-Martinez O, García-Montero C, Alvarez-Mon M, Morales I, Mora F, Quintero J, Ortega M, Perez-Araluce R, Alvarez-Mon M. Multilingual analysis of public discourse on opioid and non-opioid analgesics through social media: a cross-sectional infodemiological study. BMC Medical Research Methodology 2026;26(1) View