MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform

TL;DR

This study introduces MythTriage, a scalable system for detecting opioid use disorder myths on YouTube, combining expert validation and an efficient triage pipeline to identify misinformation at scale and inform public health efforts.

Contribution

The paper presents MythTriage, a novel scalable pipeline that combines lightweight models and large language models for efficient misinformation detection on YouTube about OUD.

Findings

Achieved 0.86 macro F1-score in myth detection.

Reduced annotation time and costs by over 76%.

Uncovered persistent myths on YouTube and provided insights for moderation.

Abstract

Understanding the prevalence of misinformation in health topics online can inform public health policies and interventions. However, measuring such misinformation at scale remains a challenge, particularly for high-stakes but understudied topics like opioid-use disorder (OUD)--a leading cause of death in the U.S. We present the first large-scale study of OUD-related myths on YouTube, a widely-used platform for health information. With clinical experts, we validate 8 pervasive myths and release an expert-labeled video dataset. To scale labeling, we introduce MythTriage, an efficient triage pipeline that uses a lightweight model for routine cases and defers harder ones to a high-performing, but costlier, large language model (LLM). MythTriage achieves up to 0.86 macro F1-score while estimated to reduce annotation time and financial cost by over 76% compared to experts and full LLM labeling. We analyze 2.9K search results and 343K recommendations, uncovering how myths persist on YouTube and offering actionable insights for public health and platform moderation.