mlsanityData to 14 Oct 2026

Supporters and Skeptics: LLM-based Analysis of Engagement with Mental Health (Mis)Information Content on Video-sharing Platforms

arXiv 2407.02662 ↗First posted 2 Jul 2024cs.SI cs.CL cs.CYcited by 4

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Abstract (arXiv)

Over one in five adults in the US lives with a mental illness. In the face of a shortage of mental health professionals and offline resources, online short-form video content has grown to serve as a crucial conduit for disseminating mental health help and resources. However, the ease of content creation and access also contributes to the spread of misinformation, posing risks to accurate diagnosis and treatment. Detecting and understanding engagement with such content is crucial to mitigating their harmful effects on public health. We perform the first quantitative study of the phenomenon using YouTube Shorts and Bitchute as the sites of study. We contribute MentalMisinfo, a novel labeled mental health misinformation (MHMisinfo) dataset of 739 videos (639 from Youtube and 100 from Bitchute) and 135372 comments in total, using an expert-driven annotation schema. We first found that few-shot in-context learning with large language models (LLMs) are effective in detecting MHMisinfo videos. Next, we discover distinct and potentially alarming linguistic patterns in how audiences engage with MHMisinfo videos through commentary on both video-sharing platforms. Across the two platforms, comments could exacerbate prevailing stigma with some groups showing heightened susceptibility to and alignment with MHMisinfo. We discuss technical and public health-driven adaptive solutions to tackling the "epidemic" of mental health misinformation online.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 10 of its 63 references that are in the map. First posted on arXiv 2 Jul 2024.

Builds on

10 of 63 references.

References · 10

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
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  10. 2020What is BitChute? Characterizing the "Free Speech" Alternative to YouTubecited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Survey and Experiments on Mental Disorder Detection via Social Media: From Large Language Models and RAG to Agentscited by 6
  2. 2025MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platformcited by 3
  3. 2025Incongruent Positivity: When Miscalibrated Positivity Undermines Online Supportive Conversationscited by 1
  4. 2026Evaluating Health Misinformation in Low-Resource Languages: Integrating Small Language Models with a Culturally-Sensitive Responsible NLP Framework (Bangla as a Case Study)cited by 0