mlsanityData to 14 Oct 2026

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

arXiv 2506.00308 ↗First posted 30 May 2025cs.CY cs.AI cs.CLcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 36 of its 95 references that are in the map. First posted on arXiv 30 May 2025.

Builds on

36 of 95 references.

References · 36

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  4. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  5. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  6. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  7. 2017Selective Classification for Deep Neural Networkscited by 852
  8. 2017A systematic study of the class imbalance problem in convolutional neural networkscited by 513
  9. 2024Large Language Models for Data Annotation and Synthesis: A Surveycited by 205
  10. 2024MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoningcited by 154
  11. 2023Cognitive Reframing of Negative Thoughts through Human-Language Model Interactioncited by 77
  12. 2019Auditing Radicalization Pathways on YouTubecited by 53
24 more
  1. 2022Model Cascading: Towards Jointly Improving Efficiency and Accuracy of NLP Systemscited by 44
  2. 2022Handling and Presenting Harmful Text in NLP Researchcited by 43
  3. 2020Multi-Hop Fact Checking of Political Claimscited by 37
  4. 2024SLM-Mod: Small Language Models Surpass LLMs at Content Moderationcited by 27
  5. 2024"They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversationscited by 25
  6. 2024Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalancecited by 17
  7. 2024Lived Experience Not Found: LLMs Struggle to Align with Experts on Addressing Adverse Drug Reactions from Psychiatric Medication Usecited by 17
  8. 2023Exploring ChatGPT for Toxicity Detection in GitHubcited by 16
  9. 2023What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situationscited by 15
  10. 2023Modeling Empathic Similarity in Personal Narrativescited by 14
  11. 2022TangoBERT: Reducing Inference Cost by using Cascaded Architecturecited by 13
  12. 2023Evaluating Large Language Models for Health-related Queries with Presuppositionscited by 12
  13. 2021Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformationcited by 11
  14. 2024LLM Chain Ensembles for Scalable and Accurate Data Annotationcited by 11
  15. 2022BabyBear: Cheap inference triage for expensive language modelscited by 10
  16. 2022An Empirical Investigation of Personalization Factors on TikTokcited by 9
  17. 2023Causally estimating the effect of YouTube's recommender system using counterfactual botscited by 8
  18. 2024A Comprehensive Study on NLP Data Augmentation for Hate Speech Detection: Legacy Methods, BERT, and LLMscited by 7
  19. 2024OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistantscited by 7
  20. 2025Limited Effectiveness of LLM-based Data Augmentation for COVID-19 Misinformation Stance Detectioncited by 7
  21. 2024LLM Confidence Evaluation Measures in Zero-Shot CSS Classificationcited by 6
  22. 2022Deradicalizing YouTube: Characterization, Detection, and Personalization of Religiously Intolerant Arabic Videoscited by 4
  23. 2024Supporters and Skeptics: LLM-based Analysis of Engagement with Mental Health (Mis)Information Content on Video-sharing Platformscited by 4
  24. 2023Assessing enactment of content regulation policies: A post hoc crowd-sourced audit of election misinformation on YouTubecited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarioscited by 5
  2. 2026Persona-Grounded Safety Evaluation of AI Companions in Multi-Turn Conversationscited by 4
  3. 2025Who's Asking? Simulating Role-Based Questions for Conversational AI Evaluationcited by 2