MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform (2025.emnlp-main)
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| Challenge: | 108K drug overdose deaths in 2022, according to NIDA . |
| Approach: | They propose a large-scale study of OUD-related myths on YouTube with clinical experts to validate 8 pervasive myths and release an expert-labeled video dataset. |
| Outcome: | The proposed model reduces annotation time and cost by over 76% compared to experts and full LLM labeling. |
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Chenghao Yang, Tuhin Chakrabarty, Karli Hochstatter, Melissa Slavin, Nabila El-Bassel, Smaranda Muresan
| Challenge: | Experimental results show that identifying the phases of opioid use disorder is highly contextual and challenging. |
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| Challenge: | Existing literature on the validity of proxy-based methods for annotating mental health status in social media has raised new concerns regarding their use in clinical applications. |
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BigTokDetect: A Clinically-Informed Vision–Language Modeling Framework for Detecting Pro-Bigorexia Videos on TikTok (2026.eacl-long)
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Minh Duc Chu, Kshitij Pawar, Zihao He, Roxanna Sharifi, Ross M. Sonnenblick, Magdalayna Curry, Laura DAdamo, Lindsay Young, Stuart Murray, Kristina Lerman
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| Challenge: | Fallacy detection is an open challenge in NLP and has shown to be intrinsically difficult for both humans and machines. |
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MentalHelp: A Multi-Task Dataset for Mental Health in Social Media (2024.lrec-main)
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Nishat Raihan, Sadiya Sayara Chowdhury Puspo, Shafkat Farabi, Ana-Maria Bucur, Tharindu Ranasinghe, Marcos Zampieri
| Challenge: | Annotating social media data for mental health disorders is expensive and time-consuming, limiting their size and scope. |
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From Generation to Detection: A Multimodal Multi-Task Dataset for Benchmarking Health Misinformation (2025.findings-emnlp)
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| Challenge: | Infodemics and health misinformation have significant negative impact on individuals and society . generative AI has significantly accelerated the spread and expanded the reach of health misinfo . |
| Approach: | MM-Health is a large scale multimodal misinformation dataset in the health domain . it includes human-generated multimodal information and AI-generated multiplemodal information . |
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Beyond Detection: A Defend-and-Summarize Strategy for Robust and Interpretable Rumor Analysis on Social Media (2023.emnlp-main)
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Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts (2023.emnlp-main)
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| Challenge: | Existing mental disease detection methods are not backed by domain knowledge and thus fail to produce interpretable results. |
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Automatic Detection of Stigmatizing Uses of Psychiatric Terms on Twitter (2022.lrec-1)
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| Challenge: | Psychiatry and people suffering from mental disorders have often been given a pejorative label that induces social rejection. |
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Telling a Lie: Analyzing the Language of Information and Misinformation during Global Health Events (2022.lrec-1)
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| Challenge: | a new dataset is available to stimulate research on health misinformation . linguistic characteristics of health misinfonia are unique to COVID-19 and other events . |
| Approach: | They propose a new dataset that analyzes health misinformation at scale . it includes 2.8 million news articles and social media posts covering diseases . authors propose an annotation framework that allows for strong agreement between annotators . |
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