社交媒体帖子中的多标签心理健康分类具有多视角提示组合和辅助自我监督
Cheng-Ying Hsieh1, Qing-Yuan Ye2, Feng-Chi Liu3
1Department of Pharmacology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Scientific reports
|December 5, 2025
概括
这项研究引入了一种新的多任务框架,以准确地分类社交媒体文本中的多种心理健康状态. 该方法通过提高多标签分类准确性来增强对焦虑和抑郁的理解.
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:
- 焦虑和抑郁是全球重要的健康问题.
- 社交媒体平台越来越多地用于表达情绪和个人斗争.
- 来自社交媒体的文本数据通常包含多个同时存在的心理状态,这构成了多个标签分类的挑战.
研究的目的:
- 开发一个先进的多任务框架,用于在社交媒体中对心理健康状况的多标签分类.
- 通过多视角提示,增强对心理健康相关内容的语义理解.
- 使用自主监督学习技术来提高分类模型的稳定性.
主要方法:
- 使用了一种预先训练的语言模型骨干.
- 一个多视角提示设计模块结合了社会学,心理学和教育观点.
- 一个基于扰乱的自我监督学习模块包括辅助任务,如预测句子修改 (插入,交换,删除).
主要成果:
- 拟议的多任务框架在MultiWD数据集上的所有基线方法相比显示出更高的性能.
- 实验涵盖了六个不同的健康维度,验证了该模型在各种心理健康指标上的有效性.
- 废弃研究证实了多视角提示设计和自我监督学习模块的重大贡献.
结论:
- 开发的多任务框架有效地解决了社交媒体上心理健康文本多标签分类的挑战.
- 多视角提示和自我监督学习的整合大大提高了模型的性能和稳定性.
- 这种方法在通过数字通信来理解和监测心理健康的计算方法中提供了有希望的进步.
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