Explainable and Interactive LLMs-Augmented Depression Detection in Social Media

Depression detection based on social media content has received increasing attention in recent years, as it allows for early diagnosis before the user’s psychological state deteriorates. Although traditional methods of depression detection can provide a classification of whether the user is depressed or not, they cannot provide human-like explanations and interactions. In this article, we propose a next-generation paradigm for depression detection, namely an interpretable and interactive depression detection system based on large language models (LLMs). The proposed system not only yields a final diagnosis result, but also offers diagnostic evidence grounded in established diagnostic criteria. Furthermore, it enables users to engage in natural language dialogue with the system, facilitating a more personalized understanding of their mental state based on their social media content. The interactive dialogue allows for the provision of tailored recommendations, which users can utilize to enhance their well-being. In constructing the entire system, we also addressed some nontrivial challenges. First, we introduced the chain of thoughts technique and professional depression diagnostic criteria when constructing the prompts, enabling our system to make decisions based on professional diagnosis criteria and provide explanations. Second, LLMs are incapable of processing excessively long contextual texts, and the accumulated posts of a single user may amount to tens of thousands of words. To overcome this limitation, we integrated a tweet selector that selects the part of posts for diagnosis. The experiments demonstrate that our depression detection system achieves the best performance across various settings, including full data setting, few-shot setting, zero-shot setting, independent-identical-distribution (IID) setting, and out-of-distribution (OOD) setting. Additionally, case studies reveal the explanation and interactivity of our system.