Scholarlink

<p>Anxiety and depression have become major global health concerns. With the rapid rise of social media, people increasingly share emotions and personal struggles through posts, which often convey multiple mental states simultaneously. To address this multi-label classification challenge in mental health texts, this study proposes a multi-task framework with two main modules, a multi-perspective prompt design module and a perturbation-based self-supervised learning module, based on a pre-trained language model backbone. Prompts from sociological, psychological, and educational perspectives are used to enhance semantic understanding. To improve model robustness, we formulate self-supervised auxiliary tasks where the model predicts whether a sentence has undergone insertion, swap, or deletion. Experiments on the MultiWD dataset, covering six wellness dimensions, show that our method outperforms all baselines. Furthermore, ablation studies explore the impact of different training configurations and confirm the critical contributions of both proposed modules.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-label mental health classification in social media posts with multi-perspective prompt ensemble and auxiliary self-supervision

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月5日

查看全文
  • Cheng-Ying Hsieh,
  • Qing-Yuan Ye,
  • Feng-Chi Liu,
  • Xin Wang,
  • Cheng-Hsiung Lee,
  • Ching-Sheng Lin

摘要

Anxiety and depression have become major global health concerns. With the rapid rise of social media, people increasingly share emotions and personal struggles through posts, which often convey multiple mental states simultaneously. To address this multi-label classification challenge in mental health texts, this study proposes a multi-task framework with two main modules, a multi-perspective prompt design module and a perturbation-based self-supervised learning module, based on a pre-trained language model backbone. Prompts from sociological, psychological, and educational perspectives are used to enhance semantic understanding. To improve model robustness, we formulate self-supervised auxiliary tasks where the model predicts whether a sentence has undergone insertion, swap, or deletion. Experiments on the MultiWD dataset, covering six wellness dimensions, show that our method outperforms all baselines. Furthermore, ablation studies explore the impact of different training configurations and confirm the critical contributions of both proposed modules.