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.

本文提出了一种新颖的方法，将大型语言模型（LLM）与可解释的人工智能（XAI）和类似 ChatGPT 的对话代理相结合，以解决对社交媒体上表达的抑郁症状进行及时检测的挑战。通过将 BERT 的 Twitter 特定变体 BERTweet 集成到 BERT-XDD 模型中，实现了解释能力，并通过掩码注意力提供分类和解释。利用 ChatGPT 将技术解释转化为可读性强的评论，进一步提高了可解释性。我们的方法为可解释性抑郁症检测提供了一种有效且模块化的方法，有助于发展具有社会责任感的数字平台，在合格的保健专业人员指导下促进对心理健康挑战的早期干预和支持。