In the digital era, the prevalence of depressive symptoms expressed on social media has raised serious concerns, necessitating advanced methodologies for timely detection. This paper addresses the challenge of interpretable depression detection by proposing a novel methodology that effectively combines Large Language Models (LLMs) with eXplainable Artificial Intelligence (XAI) and conversational agents like ChatGPT. In our methodology, explanations are achieved by integrating BERTweet, a Twitter-specific variant of BERT, into a novel self-explanatory model, namely BERT-XDD, capable of providing both classification and explanations via masked attention. The interpretability is further enhanced using ChatGPT to transform technical explanations into human-readable commentaries. By introducing an effective and modular approach for interpretable depression detection, our methodology can contribute to the development of socially responsible digital platforms, fostering early intervention and support for mental health challenges under the guidance of qualified healthcare professionals.

翻译：在数字时代，社交媒体上表达的抑郁症状日益普遍，引发了严重关切，亟需先进方法进行及时检测。本文针对可解释性抑郁检测的挑战，提出了一种新型方法论，有效结合大型语言模型（LLM）、可解释人工智能（XAI）以及ChatGPT等对话代理。在本方法中，通过将面向Twitter的BERT变体BERTweet集成到新型自解释模型BERT-XDD中，利用掩码注意力机制实现分类与解释的双重功能。进一步借助ChatGPT将技术性解释转化为人类可读的评论，从而增强可解释性。通过引入这种高效且模块化的可解释性抑郁检测方法，本研究可为构建具有社会责任感的数字平台作出贡献，在合格医疗专业人员的指导下促进心理健康问题的早期干预与支持。