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.

翻译：美国超过五分之一的成年人患有精神疾病。面对心理健康专业人员及线下资源的短缺，在线短视频内容已成为传播心理健康帮助与资源的关键渠道。然而，内容创作与获取的便利性也助长了错误信息的传播，对准确诊断与治疗构成风险。检测并理解对此类内容的参与行为，对于减轻其对公共健康的有害影响至关重要。我们以YouTube Shorts和Bitchute为研究平台，首次对该现象进行了定量研究。我们贡献了MentalMisinfo——一个新颖的标注心理健康错误信息数据集，包含739个视频（其中639个来自YouTube，100个来自Bitchute）及总计135372条评论，采用专家驱动的标注框架。我们首先发现，基于大语言模型的少样本上下文学习能有效检测心理健康错误信息视频。其次，我们通过分析两个视频分享平台的评论，发现了受众参与心理健康错误信息视频时存在的独特且可能令人担忧的语言模式。在两个平台上，评论可能加剧普遍存在的污名化现象，部分群体显示出对心理健康错误信息的更高易感性与认同度。我们讨论了应对线上心理健康错误信息"流行病"的技术性及公共卫生驱动的适应性解决方案。