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

Abstract

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.

Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach

“…To create a comprehensive list of relevant subreddits, we used a methodology similar to the previous research [ 36 ]. We began by compiling an extensive lexicon of terms related to substance use, focusing on keywords that encompass various facets of the experience and treatment of substance use disorders.…”

Investigating Substance Use via Reddit: Systematic Scoping Review

“…Anonymous data [18,19,[21][22][23][26][27][28][29][30][31]33,34,38,45,55,57,60,61,65,[72][73][74][75] 24 (40) Reddit is one of the most popular social networking sites.…”

“…Large-scale data sets [24][25][26]34,45,49,58,60,74] 9 (15) Reddit provides a public application programming interface for easier data collection.…”

Global utilization of online information for substance use disorder: An infodemiological study of Google and Wikipedia from 2004 to 2022

“…Specifically, the exponential spread of health misinformation and disinformation. The World Health Organization (World Health Organization, 2020) coins this phenomenon as “infodemic.” Aside from COVID‐19, infodemics have been found in other conditions, including substance use disorders (ElSherief et al, 2021). Health infodemics fuel anti‐science and anti‐medicine sentiments (i.e., anti‐vaxxer movements), diminish trust in allied health professions and institutions, and deter the acceptability and effectiveness of clinical and public interventions for health problems (World Health Organization, 2020).…”

“…The World Health Organization (World Health Organization, 2020) coins this phenomenon as "infodemic." Aside from COVID-19, infodemics have been found in other conditions, including substance use disorders (ElSherief et al, 2021).…”