Online Myths on Opioid Use Disorder: A Comparison of Reddit and Large Language Model

Online communities on Reddit are a popular choice among people with opioid use disorder (OUD) to seek information on drug use, withdrawal symptoms, and recovery. LLM-powered chatbots (e.g., ChatGPT) are widely being adopted as question-answer systems for health-related queries. However, such online health information seeking could potentially be hindered by myths and misinformation on OUD, misleading or causing genuine harm to people with OUD. In this work, we examine the prevalence of 5 OUD-related myths, on treatment models and patient characteristics, within human- (taken from Reddit) and LLM-generated responses to queries on OUD. We further explore the framing strategies used within responses (both human- and LLM-generated) promoting and countering the myths. We found that all 5 myths were more widespread within human-generated responses. In addition, myth-promoting responses adopted trustworthy and authoritative framings, compared to knowledge-imparting linguistic cues within those countering the myths. Our work offers recommendations to reduce online OUD misinformation.

Authors

Institutions

Publication Details

Journal
Proceedings of the International AAAI Conference on Web and Social Media
Published
2025-06-07
DOI
https://doi.org/10.1609/icwsm.v19i1.35870
Citations
1
Primary Topic
Sentiment Analysis and Opinion Mining
Type
article
Field-Weighted Citation Impact
2.48

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Online Myths on Opioid Use Disorder: A Comparison of Reddit and Large Language Model

Hayoung Jung, Tanushree Mitra, Mai ElSherief, Shravika Mittal et al.
1 citations
Proceedings of the International AAAI Conference on Web and Social Media
Sentiment Analysis and Opinion Mining
2.48
article

Online Myths on Opioid Use Disorder: A Comparison of Reddit and Large Language Model

Hayoung Jung, Tanushree Mitra, Mai ElSherief, Shravika Mittal, Munmun De Choudhury
article en
1 citations

Abstract

Online communities on Reddit are a popular choice among people with opioid use disorder (OUD) to seek information on drug use, withdrawal symptoms, and recovery. LLM-powered chatbots (e.g., ChatGPT) are widely being adopted as question-answer systems for health-related queries. However, such online health information seeking could potentially be hindered by myths and misinformation on OUD, misleading or causing genuine harm to people with OUD. In this work, we examine the prevalence of 5 OUD-related myths, on treatment models and patient characteristics, within human- (taken from Reddit) and LLM-generated responses to queries on OUD. We further explore the framing strategies used within responses (both human- and LLM-generated) promoting and countering the myths. We found that all 5 myths were more widespread within human-generated responses. In addition, myth-promoting responses adopted trustworthy and authoritative framings, compared to knowledge-imparting linguistic cues within those countering the myths. Our work offers recommendations to reduce online OUD misinformation.

Proceedings of the International AAAI Conference on Web and Social MediaVol. 19
Georgia Institute of Technology (US), University of Washington (US), Universidad del Noreste (MX)
National Science Foundation, Centers for Disease Control and Prevention, National Institute on Drug Abuse
Openalex Percentile: Top 10%
Sentiment Analysis and Opinion Mining
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Online Myths on Opioid Use Disorder: A Comparison of Reddit and Large Language Model — Hayoung Jung, Tanushree Mitra, et al. · Proceedings of the International AAAI Conference on Web and Social Media (2025) | TGRS Research Map | TGRS