MAMA-Memeia! Multi-Aspect Multi-Agent Collaboration for Depressive Symptoms Identification in Memes
This addresses the challenge of detecting mental health issues in online content for social media users and platforms, representing an incremental advance with a specific domain focus.
The paper tackles the problem of identifying depressive symptoms in memes shared on social media by introducing MAMAMemeia, a multi-agent multi-aspect framework based on Cognitive Analytic Therapy, which improves state-of-the-art performance by 7.55% in macro-F1 and sets a new benchmark against over 30 methods.
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.