DepGLM:基于大型语言模型的社交媒体上的抑郁程度识别
Jingfang Liu1, Peng Ding1, Jie Chen1
1School of Management, Shanghai University, Shanghai, China.
Digital health
|December 22, 2025
概括
这项研究介绍了DepGLM,这是一个用于从社交媒体文本中识别抑郁症严重性的大型语言模型. DepGLM-tc获得了84%的F1评分,超过了现有的方法,更准确地识别抑郁症.
科学领域:
- 计算语言学计算语言学
- 心理健康信息学心理健康信息学
- 自然语言处理自然语言处理.
背景情况:
- 抑郁症是一个全球性的心理健康问题,需要早期发现和干预.
- 社交媒体数据提供了早期抑郁症预警的潜力,但传统方法缺乏准确性和解释性.
- 现有的研究经常使用二元分类,缺少细微的抑郁症严重程度识别.
研究的目的:
- 开发一种使用多级别文本分类和生成的细粒度抑郁程度识别模型.
- 探索在中国社交媒体背景下增强身份识别.
- 创建新的数据集,用于抑郁症程度的分类和可解释的指令遵循.
主要方法:
- 模拟抑郁程度的识别作为多层次的文本分类和生成任务.
- 构建了抑郁微博数据集和一个多层次可解释指令数据集.
- 微调了ChatGLM3的大型语言模型,以创建DepGLM (DepGLM-tc和DepGLM-tg).
主要成果:
- 德普GLM-tc获得了84%的加权F1得分,超过了当前最先进的歧视模型.
- 据DepGLM-tg证明,其准确性与SOTA的差异化方法相提并论.
- 文本生成模型产生了高质量的解释,类似于ChatGPT.
结论:
- 精心调整的大型语言模型为识别抑郁症严重程度提供了卓越的准确性.
- 开发的DepGLM模型显示,在抑郁症分类任务中性能提高.
- 这种方法提升了使用社交媒体数据检测微妙的抑郁症.
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