Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach

• Published in: Journal of medical Internet research 2021-12, Vol.23 (12), p.e30753-e30753
• Main Authors: ElSherief, Mai, Sumner, Steven A, Jones, Christopher M, Law, Royal K, Kacha-Ochana, Akadia, Shieber, Lyna, Cordier, LeShaundra, Holton, Kelly, De Choudhury, Munmun
• Format: Article
• Language: English
• Subjects: Addictions; Cognitive style; Communication; Coronaviruses; COVID-19 vaccines; Disease; Drug use; Drugs; English language; Epidemics; False information; Feasibility; Humans; Internet; Machine Learning; Medical research; Misinformation; Mitigation; Mythology; Narcotics; Opioid-Related Disorders - drug therapy; Opioid-Related Disorders - epidemiology; Opioids; Original Paper; Pandemics; Prescription drugs; Prevalence; Public health; Social Media; Social networks; Substance abuse treatment; Substance use disorder; Teams; Uptake; User generated content
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/30753

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. 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. 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. 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. 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.