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.

翻译：近年来，表情包已从单纯的幽默交流媒介演变为用户自由便捷表达多元情感的工具。随着表情包在抑郁情绪表达中的使用日益增长，本研究针对在线社交媒体平台用户分享的表情包所呈现的抑郁症状进行识别分析。我们提出RESTOREx数据集，该资源通过大语言模型生成并经人工标注的解释文本，为社交媒体表情包中的抑郁症状检测提供重要支持。我们进一步提出MAMAMemeia框架——一个基于认知分析疗法能力模型的协作式多智能体多维度讨论框架。该框架在宏观F1分数上较当前最优方法提升7.55%，在与30余种方法的对比中确立了新的性能基准。