TY  - JOUR
AU  - ElSherief, Mai
AU  - Sumner, Steven
AU  - Krishnasamy, Vikram
AU  - Jones, Christopher
AU  - Law, Royal
AU  - Kacha-Ochana, Akadia
AU  - Schieber, Lyna
AU  - De Choudhury, Munmun
PY  - 2024
DA  - 2024/2/23
TI  - Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study
JO  - JMIR Form Res
SP  - e44726
VL  - 8
KW  - addiction treatment
KW  - machine learning
KW  - misinformation
KW  - natural language processing
KW  - opioid use disorder
KW  - social media
KW  - substance use
AB  - 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. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2024/1/e44726
UR  - https://doi.org/10.2196/44726
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38393772
DO  - 10.2196/44726
ID  - info:doi/10.2196/44726
ER  -