%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 23
%N 12
%P e30753
%T Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach
%A ElSherief,Mai
%A Sumner,Steven A
%A Jones,Christopher M
%A Law,Royal K
%A Kacha-Ochana,Akadia
%A Shieber,Lyna
%A Cordier,LeShaundra
%A Holton,Kelly
%A De Choudhury,Munmun
%+ School of Interactive Computing, Georgia Institute of Technology, 756 W Peachtree St NW, Atlanta, GA, 30308, United States, 1 4043858603, munmund@gatech.edu
%K opioid use disorder
%K substance use
%K addiction treatment
%K misinformation
%K social media
%K machine learning
%K natural language processing
%D 2021
%7 22.12.2021
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: Expanding access to and use of medication for opioid use disorder (MOUD) is a key component of overdose prevention. An important barrier to the uptake of MOUD is exposure to inaccurate and potentially harmful health misinformation on social media or web-based forums where individuals commonly seek information. There is a significant need to devise computational techniques to describe the prevalence of web-based health misinformation related to MOUD to facilitate mitigation efforts. Objective: By adopting a multidisciplinary, mixed methods strategy, this paper aims to present machine learning and natural language analysis approaches to identify the characteristics and prevalence of web-based misinformation related to MOUD to inform future prevention, treatment, and response efforts. Methods: The team harnessed public social media posts and comments in the English language from Twitter (6,365,245 posts), YouTube (99,386 posts), Reddit (13,483,419 posts), and Drugs-Forum (5549 posts). Leveraging public health expert annotations on a sample of 2400 of these social media posts that were found to be semantically most similar to a variety of prevailing opioid use disorder–related myths based on representational learning, the team developed a supervised machine learning classifier. This classifier identified whether a post’s language promoted one of the leading myths challenging addiction treatment: that the use of agonist therapy for MOUD is simply replacing one drug with another. Platform-level prevalence was calculated thereafter by machine labeling all unannotated posts with the classifier and noting the proportion of myth-indicative posts over all posts. Results: Our results demonstrate promise in identifying social media postings that center on treatment myths about opioid use disorder with an accuracy of 91% and an area under the curve of 0.9, including how these discussions vary across platforms in terms of prevalence and linguistic characteristics, with the lowest prevalence on web-based health communities such as Reddit and Drugs-Forum and the highest on Twitter. Specifically, the prevalence of the stated MOUD myth ranged from 0.4% on web-based health communities to 0.9% on Twitter. Conclusions: This work provides one of the first large-scale assessments of a key MOUD-related myth across multiple social media platforms and highlights the feasibility and importance of ongoing assessment of health misinformation related to addiction treatment. 
%M 34941555
%R 10.2196/30753
%U https://www.jmir.org/2021/12/e30753
%U https://doi.org/10.2196/30753
%U http://www.ncbi.nlm.nih.gov/pubmed/34941555