用多智能体协作分析网络迷因中的抑郁症状，性能超越现有方法7.55%。
MAMA-Memeia! Multi-Aspect Multi-Agent Collaboration for Depressive Symptoms Identification in Memes
- 设计多智能体框架，模拟临床认知分析疗法进行多角度讨论。
- 在社交迷因抑郁识别任务中，宏平均F1提升7.55%，成为新基准。
- 适用于心理健康检测、AI辅助心理评估等场景。
近年来，迷因已从单纯的幽默交流工具演变为用户自由表达情绪的重要方式。随着越来越多用户通过迷因表达抑郁情绪，本文研究了社交媒体上迷因所展现的抑郁症状识别问题。我们构建了RESTOREx数据集，包含大语言模型生成与人工标注的解释，用于支持迷因抑郁症状检测。提出MAMAMemeia框架，基于认知分析疗法（CAT）能力，实现多智能体多维度协同讨论。该方法在超过30种现有方法中表现最优，宏平均F1指标提升7.55%，确立为新基准。
原文摘要 · Abstract (English)
Over the past years, memes have evolved from being exclusively a medium of humorous exchanges to one that allows users to express a range of emotions freely and easily. With the ever-growing utilization of memes in expressing depressive sentiments, we conduct a study on identifying depressive symptoms exhibited by memes shared by users of online social media platforms. We introduce RESTOREx as a vital resource for detecting depressive symptoms in memes on social media through the Large Language Model (LLM) generated and human-annotated explanations. We introduce MAMAMemeia, a collaborative multi-agent multi-aspect discussion framework grounded in the clinical psychology method of Cognitive Analytic Therapy (CAT) Competencies. MAMAMemeia improves upon the current state-of-the-art by 7.55% in macro-F1 and is established as the new benchmark compared to over 30 methods.
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