MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform
This addresses misinformation detection for public health on a major video-sharing platform, offering scalable solutions for understudied topics like opioid-use disorder.
The study tackled the challenge of detecting opioid-use disorder myths on YouTube by developing MythTriage, a scalable pipeline that combines lightweight models and LLMs, achieving up to 0.86 macro F1-score and reducing annotation costs by over 76%.
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.