ElSherief M, Sumner S, Krishnasamy V, Jones C, Law R, Kacha-Ochana A, Schieber L, De Choudhury M. Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study.
JMIR Form Res 2024;
8:e44726. [PMID:
38393772 PMCID:
PMC10924265 DOI:
10.2196/44726]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 10/01/2023] [Accepted: 11/01/2023] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND
Health misinformation and myths about treatment for opioid use disorder (OUD) are present on social media and contribute to challenges in preventing drug overdose deaths. However, no systematic, quantitative methodology exists to identify what types of misinformation are being shared and discussed.
OBJECTIVE
We developed a multistage analytic pipeline to assess social media posts from Twitter (subsequently rebranded as X), YouTube, Reddit, and Drugs-Forum for the presence of health misinformation about treatment for OUD.
METHODS
Our approach first used document embeddings to identify potential new statements of misinformation from known myths. These statements were grouped into themes using hierarchical agglomerative clustering, and public health experts then reviewed the results for misinformation.
RESULTS
We collected a total of 19,953,599 posts discussing opioid-related content across the aforementioned platforms. Our multistage analytic pipeline identified 7 main clusters or discussion themes. Among a high-yield data set of posts (n=303) for further public health expert review, these included discussion about potential treatments for OUD (90/303, 29.8%), the nature of addiction (68/303, 22.5%), pharmacologic properties of substances (52/303, 16.9%), injection drug use (36/303, 11.9%), pain and opioids (28/303, 9.3%), physical dependence of medications (22/303, 7.2%), and tramadol use (7/303, 2.3%). A public health expert review of the content within each cluster identified the presence of misinformation and myths beyond those used as seed myths to initialize the algorithm.
CONCLUSIONS
Identifying and addressing misinformation through appropriate communication strategies could be an increasingly important component of preventing overdose deaths. To further this goal, we developed and tested an approach to aid in the identification of myths and misinformation about OUD from large-scale social media content.
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