MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform
Journal:
arXiv
Published Date:
May 30, 2025
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.
