Large language model empowered explainable and interpretable mental health analysis.

Résumé

Concern about expressing depressive symptoms on social media is growing in the digital age. Traditional detection methods can identify depression but lack clear, human-understandable explanations. We present a novel methodology "InsightDep", a mental health analysis model specifically designed to detect depression in the social media users. Furthermore, the model provides explainability for the model results and facilitates the translation of the model results and explanations into easily understandable and interpretable formats for human comprehension, leveraging the capabilities of large language models (LLMs). The proposed method extends a variant of the BERT model specifically adapted for analyzing Twitter data, to an explainable and interpretable system. The model makes use of masked attention techniques to perform classification as well as making it explainable. Additionally, we incorporate the power of LLM to convert complex technical explanations into comprehensible comments that improves the interpretability of the system. When evaluated on Twitter and Reddit datasets, InsightDep achieved macro-F1/accuracy scores of 0.599/0.671 and 0.994/0.994, respectively, outperforming other state-of-the-art methods such as BERTweet, TwHIN-BERT, and DepRoBERTa. Our novel methodology focuses on the development of morally aware digital channels that enable prompt action and support for mental health concerns, under the supervision of licensed medical experts like doctors and psychologists. This methodology emphasizes the practical application in therapeutic assessments conducted by these professionals.