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

Early Results in Infodemiology and Infoveillance (59) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1573) Opioid and Related Substance Misuse (128) Infodemiology and Infoveillance (1278) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1015) Development and Evaluation of Research Methods, Instruments and Tools (439)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42162, first published .
Estimating County-Level Overdose Rates Using Opioid-Related Twitter Data: Interdisciplinary Infodemiology Study

Estimating County-Level Overdose Rates Using Opioid-Related Twitter Data: Interdisciplinary Infodemiology Study

Estimating County-Level Overdose Rates Using Opioid-Related Twitter Data: Interdisciplinary Infodemiology Study

Authors of this article:

Raphael Cuomo1, 2 Author Orcid Image ;   Vidya Purushothaman2, 3 Author Orcid Image ;   Alec J Calac1, 2 Author Orcid Image ;   Tiana McMann2, 3, 4, 5 Author Orcid Image ;   Zhuoran Li2, 3, 5 Author Orcid Image ;   Tim Mackey2, 3, 4, 5 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

Journals

  1. Tang L, Korona-Bailey J, Zaras D, Roberts A, Mukhopadhyay S, Espy S, Walsh C. Using Natural Language Processing to Predict Fatal Drug Overdose From Autopsy Narrative Text: Algorithm Development and Validation Study. JMIR Public Health and Surveillance 2023;9:e45246 View
  2. Al-Hamid A, Tudor C, Assi S. Exploring profile, effects and toxicity of novel synthetic opioids and classical opioids via Twitter: A qualitative study. Emerging Trends in Drugs, Addictions, and Health 2024;4:100139 View