Identification of Myths and Misinformation about Treatment for Opioid Use Disorder from Social Media (Preprint)

Abstract

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.

Assessing social media posts from Twitter, YouTube, Reddit, and Drugs-Forum.com for the presence of health misinformation about treatment for OUD.

We developed a multi-stage analytic pipeline to assess social media posts from Twitter, YouTube, Reddit, and Drugs-Forum.com for the presence of health misinformation about treatment for OUD. Our approach first utilized 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 results for misinformation.

We collected a total of 19,953,599 posts discussing opioid-related content across the aforementioned platforms. Our multi-stage analytic pipeline identified seven main clusters or discussion themes. Among a high-yield dataset of posts (N=303) for further public health expert review, these included discussion about potential treatments for opioid use disorder (N=90, 29.8%), the nature of addiction (N=68, 22.5%), pharmacologic properties of substances (N=52, 16.88%), injection drug use (N=36, 11.9%), pain and opioids (N=28, 9.27%), physical dependence of medications (N=22, 7.2%), and tramadol use (N=7, 2.3%). Public health expert review of content within each cluster identified the presence of misinformation and myths beyond those used as seed myths to initialize the algorithm.

Identifying and addressing misinformation through appropriate communication strategies could be an increasingly important component to 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.