Team MKC 在 CLPsych 2026：通过社交媒体时间线动态捕捉和表征心理健康变化
Team MKC at CLPsych 2026: Capturing and Characterizing Mental Health Changes through Social Media Timeline Dynamics
AI总结：
本文提出基于大语言模型的流水线，通过分析用户按时间排序的帖子，实现帖子级评估和用户级时间建模，以支持心理健康的早期检测和持续监测。
摘要：
大语言模型的最新进展推动了其在包括心理健康人工智能在内的广泛领域的应用。鉴于全球心理健康障碍的日益普遍以及专业护理的可及性有限，对可扩展的计算方法的需求不断增加，这些方法可以协助心理健康的早期检测和持续监测。在这一领域，持续的努力集中在整理特定领域的数据集，并利用它们开发能够支持整体心理健康分析的大语言模型。沿着这一方向，我们提出了一种基于大语言模型的流水线，用于对按时间顺序排列的用户帖子进行全面的心理健康分析，作为CLPsych共享任务的一部分。我们的流水线提供了一个统一的框架，可以联合实现帖子级评估和用户级时间建模。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
