用大模型分析社交媒体时间序列，追踪用户心理状态变化
Team MKC at CLPsych 2026: Capturing and Characterizing Mental Health Changes through Social Media Timeline Dynamics
- 构建统一框架，同时分析单条动态和长期心理趋势
- 基于用户发帖序列实现心理状态的连续监测与评估
- 适合关注心理健康预警与数字疗法的研究者
大型语言模型（LLMs）的进展推动其在人工智能心理健康领域的应用。面对全球精神健康障碍日益增多及专业医疗资源有限的问题，亟需可扩展的计算方法以实现早期检测与持续监测。现有研究聚焦于构建领域专用数据集，并利用这些数据开发支持整体心理状态分析的LLM。本文针对CLPsych共享任务，提出一种基于LLM的综合心理分析流程，适用于按时间顺序排列的用户发帖序列。该流程提供统一框架，能够同时实现单条帖子层面的评估与用户层面的时间动态建模。
原文摘要 · Abstract (English)
Recent advances in Large Language Models (LLMs) have motivated their adoption across a wide range of domains, including Artificial Intelligence (AI) for mental health. Given the growing prevalence of mental health disorders worldwide and the limited accessibility of professional care, there is an increasing demand for scalable computational approaches that can assist in early detection and continuous monitoring of psychological well-being. In this area, ongoing efforts have focused on curating domain-specific datasets and leveraging them to develop LLMs capable of supporting holistic mental health analysis. In line with this direction, we propose an LLM-based pipeline for comprehensive mental health analysis over sequentially ordered user posts, as part of the CLPsych shared task. Our pipeline offers a unified framework that jointly enables post-level assessment and user-level temporal modeling.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。