Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study

Mai ElSherief; Steven Sumner; Vikram Krishnasamy; Christopher Jones; Royal Law; Akadia Kacha-Ochana; Lyna Schieber; Munmun De Choudhury

doi:10.2196/44726

Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study

JMIR Form Res. 2024 Feb 23:8:e44726. doi: 10.2196/44726.

Authors

Mai ElSherief¹, Steven Sumner², Vikram Krishnasamy², Christopher Jones², Royal Law², Akadia Kacha-Ochana², Lyna Schieber², Munmun De Choudhury³

Affiliations

¹ Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States.
² Centers for Disease Control and Prevention, Atlanta, GA, United States.
³ Georgia Institute of Technology, Atlanta, GA, United States.

PMID: 38393772
PMCID: PMC10924265
DOI: 10.2196/44726

Abstract

Background: 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.

Objective: We developed a multistage analytic pipeline to assess social media posts from Twitter (subsequently rebranded as X), YouTube, Reddit, and Drugs-Forum for the presence of health misinformation about treatment for OUD.

Methods: Our approach first used 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 the results for misinformation.

Results: We collected a total of 19,953,599 posts discussing opioid-related content across the aforementioned platforms. Our multistage analytic pipeline identified 7 main clusters or discussion themes. Among a high-yield data set of posts (n=303) for further public health expert review, these included discussion about potential treatments for OUD (90/303, 29.8%), the nature of addiction (68/303, 22.5%), pharmacologic properties of substances (52/303, 16.9%), injection drug use (36/303, 11.9%), pain and opioids (28/303, 9.3%), physical dependence of medications (22/303, 7.2%), and tramadol use (7/303, 2.3%). A public health expert review of the content within each cluster identified the presence of misinformation and myths beyond those used as seed myths to initialize the algorithm.

Conclusions: Identifying and addressing misinformation through appropriate communication strategies could be an increasingly important component of 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.

Keywords: addiction treatment; machine learning; misinformation; natural language processing; opioid use disorder; social media; substance use.

©Mai ElSherief, Steven Sumner, Vikram Krishnasamy, Christopher Jones, Royal Law, Akadia Kacha-Ochana, Lyna Schieber, Munmun De Choudhury. Originally published in JMIR Formative Research (https://formative.jmir.org), 23.02.2024.