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Updated: Sep 10, 2025

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支持者和怀疑者:基于LLM的心理健康参与分析
Viet Cuong Nguyen1, Mini Jain1, Abhijat Chauhan1
1Georgia Institute of Technology.
概括
在YouTube短片和Bitchute等平台上迅速传播错误的心理健康信息. 这项研究发现大型语言模型可以检测错误信息,
科学领域:
- 数字健康
- 公共卫生
- 计算语言学
背景情况:
- 在线短视频内容是心理健康信息的不断增长来源.
- 错误信息的迅速传播对准确的诊断和治疗构成风险.
- 需要了解和减轻在线心理健康错误信息的影响.
研究的目的:
- 在YouTube短片和Bitchute上对心理健康错误信息进行定量研究.
- 开发和评估检测MHMisinfo视频的方法.
- 分析与MHMisinfo的观众互动模式.
主要方法:
- 创建了MentalMisinfo数据集,包括来自YouTube短片和Bitchute的739个视频和135,372条评论.
- 使用大语言模型 (LLM) 进行背景学习,用于MHMisinfo视频检测.
- 用户评论的语言分析以了解与MHMisinfo的互动.
主要成果:
- 在检测MHMisinfo视频方面,
- 很明显的语言模式显示了观众与MHMisinfo的接触.
- 评论MHMisinfo视频可能会加剧心理健康的耻辱感,
结论:
- 提供一个有前途的工具来识别MHMisinfo.
- 观众与MHMisinfo的接触带来了重大的公共卫生挑战.
- 需要适应性的技术和公共卫生策略来打击在线 MHMisinfo.
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