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

Early Results in Infodemiology and Infoveillance (104) Misinformation and Disinformation Outbreaks and Information Prevalence Studies (93) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1911) Infodemiology and Infoveillance (1558) Public Health (247) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (215) Substance Abuse (530) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1139) Opioid and Related Substance Misuse (200) Pharmacovigilance (171) Quality/Credibility of eHealth Information and Trust Issues (654)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44726, first published .
Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study

Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study

Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study

Authors of this article:

Mai ElSherief1 Author Orcid Image ;   Steven Sumner2 Author Orcid Image ;   Vikram Krishnasamy2 Author Orcid Image ;   Christopher Jones2 Author Orcid Image ;   Royal Law2 Author Orcid Image ;   Akadia Kacha-Ochana2 Author Orcid Image ;   Lyna Schieber2 Author Orcid Image ;   Munmun De Choudhury3 Author Orcid Image
Article Authors Cited by (9) Tweetations (5) Metrics

Journals

  1. Jantzen A, Hall S, Lai B, Cunningham J, Odell L. Identification of patients on chronic prescription opioids at risk for opioid use disorder using pharmacy claims data. Journal of Healthcare Risk Management 2024;44(2):14 View
  2. Basak M, Sharif O, Lord S, Borodovsky J, Marsch L, Springer S, Nunes E, Brackett C, Archibald L, Preum S. Information Needs for Opioid Use Disorder Treatment Using Buprenorphine Product: Qualitative Analysis of Suboxone-Focused Reddit Data. Journal of Medical Internet Research 2025;27:e68886 View
  3. Carpenter K, Nguyen A, Smith D, Samori I, Humphreys K, Lembke A, Kiang M, Eichstaedt J, Altman R, Villanueva C. Which social media platforms facilitate monitoring the opioid crisis?. PLOS Digital Health 2025;4(4):e0000842 View
  4. Seo H, Borah P, Sutherland A, Lee D, Austin E. News-Finds-Me Perception, Substance Use Misinformation Beliefs, and Nonmedical Prescription Drug Use: The Moderating Effect of Gender. Substance Use & Misuse 2025;60(12):1799 View
  5. Irpan I, Subagyo S, Anwar M, Paramitha S, Widianingsih S, Purnomo E. Junior High Students' Awareness Assessment of Narcotics, Psychotropics, and Addictive Substances Dangers in Bandung. Salud, Ciencia y Tecnología 2025;5:2066 View
  6. Zhou J, Chen A, Shah D, Schwab-Reese L, De Choudhury M. A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Health. Proceedings of the ACM on Human-Computer Interaction 2025;9(7):1 View
  7. Cianciulli A, Santoro E, Manente R, Pacifico A, Quagliarella S, Bruno N, Schettino V, Boccia G. Artificial Intelligence and Digital Technologies Against Health Misinformation: A Scoping Review of Public Health Responses. Healthcare 2025;13(20):2623 View
  8. Han E, Feng M, Ling P. Building an analytical framework for tobacco-related information on social media: an exploratory analysis with generative AI assistance. BMC Public Health 2025;25(1) View
  9. Mittal S, Shah D, Do S, ElSherief M, Mitra T, De Choudhury M. Exposure to content written by large language models can reduce stigma around opioid use disorder. npj Artificial Intelligence 2025;1(1) View