检测社交媒体上的心理障碍：一种ChatGPT增强的可解释方法
Detecting mental disorder on social media: a ChatGPT-augmented explainable approach
AI总结：
本研究提出一种结合LLM、XAI和ChatGPT的可解释抑郁症检测方法，通过BERT-XDD模型实现分类与解释，并转化为人类可读评论，以支持早期干预。
摘要：
在数字时代，社交媒体上表达的抑郁症状的普遍性引发了严重关注，需要先进的方法进行及时检测。本文通过提出一种新颖的方法论来解决可解释抑郁症检测的挑战，该方法有效地将大型语言模型（LLMs）与可解释人工智能（XAI）以及像ChatGPT这样的对话代理相结合。在我们的方法论中，通过将BERTweet（一种针对Twitter的BERT变体）整合到一个新颖的自解释模型（即BERT-XDD）中来实现解释，该模型能够通过掩码注意力同时提供分类和解释。可解释性进一步通过使用ChatGPT将技术解释转化为人类可读的评论来增强。通过引入一种有效且模块化的可解释抑郁症检测方法，我们的方法论有助于开发对社会负责的数字平台，在合格医疗保健专业人员的指导下，促进对心理健康挑战的早期干预和支持。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
