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利用人工智能捕捉在线心理健康耻辱:一个范围审查

Evan Wright¹, Chana T Fisch², Howard H Goldman³

¹Columbia University Vagelos College of Physicians and Surgeons, 630 West 168th Street, New York, NY, 10032, USA.

Psychiatry research

|March 9, 2026

概括

人工智能 (AI) 可以检测在线心理健康污名,但当前的方法往往错过了微妙的语言,缺乏多样化的验证. 未来的人工智能研究应该专注于对有效的反耻辱干预措施的细微分析.

科学领域:

计算语言学计算语言学
心理健康研究心理健康研究
人工智能的人工智能

背景情况:

在线心理健康耻辱是获得护理的重要障碍.
人工智能 (AI) 提供了可扩展检测和分析在线耻辱的潜力.
来自人工智能分析的见解可以为减少耻辱的策略提供信息.

研究的目的:

进行对人工智能应用进行范围审查,以分析在线心理健康污名.
确定这一领域的关键方法,研究缺口和未来方向.
绘制有关人工智能和心理健康污名化的现有文献的地图.

主要方法:

按照PRISMA-ScR指南进行系统的文献搜索.
搜索了主要数据库 (科学网,PubMed,PsycInfo,IEEE Xplore) 的英语语言研究 (2015-2025年).
包括应用人工智能的研究来分析在线关于心理健康耻辱的公共话语.

主要成果:

33项研究符合纳入标准,利用人工智能进行污名检测和主题识别.
情感分析和主题建模是最常见的AI技术.
对于微妙的污名化线索 (例如刺),有限地使用上下文感知的人工智能,并专注于英语内容/常见条件.

结论:

人工智能显示出大规模在线心理健康耻辱分析的前景.
当前的人工智能方法可能过于简化了耻辱,缺乏广泛的验证,并未充分利用先进的模型.
未来的研究需要对人工智能模型进行细微的语言和多种背景的改进,以改进干预.

关键词:

人工智能的人工智能是人工智能.心理健康心理健康社交媒体社交媒体污名化是一种耻辱.

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