JMIR Publications

ABSTRACT

Background:

Expanding access to and use of medication for opioid use disorder (MOUD) is a key component of overdose prevention. One important barrier to uptake of MOUD is exposure to inaccurate and potentially harmful health misinformation in social media or online forums, where individuals commonly seek information. There is a significant need to devise computational techniques to describe the prevalence of online health misinformation related to MOUD to facilitate mitigation efforts.

Objective:

Adopting a multidisciplinary, mixed methods strategy, this paper presents machine learning and natural language analysis approaches to identify the characteristics and prevalence of online misinformation related to MOUD in order to inform future prevention, treatment, and response efforts.

Methods:

The team harnessed English language public social media posts and comments from Twitter (6,365,245 posts), YouTube (99,386 posts), Reddit (13,483,419 posts), and Drugs-Forum.com (5,549 posts). Leveraging public health expert annotations on a sample of 2,400 of these social media posts that were found to be semantically most similar to a variety of prevailing opioid use disorder (OUD)-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 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 around treatment myths about OUD with an accuracy of 91% and an AUC of 0.9, including how these discussions vary across platforms in terms of prevalence and linguistic characteristics, with the lowest prevalence on Online Health Communities (OHCs) such as Reddit and Drugs-Forum and the highest on Twitter. Specifically, the prevalence of the stated MOUD myth ranged from 0.4% on Online Health Communities (OHCs) to 0.9% on Twitter.

Conclusions:

This work provides one of the first large scale assessments of a key MOUD-related myths across multiple social media platforms and highlights the feasibility and importance in ongoing assessment of health misinformation related to addiction treatment.