BERT-XDD is a deep learning methodology for effective and interpretable depression detection from social media posts.
- It effectively combines Large Language Models (LLMs) with eXplainable Artificial Intelligence (XAI) and conversational agents like ChatGPT.
- Explanations are achieved by integrating BERTweet into a self-explainable architecture that relies on the masked attention mechanism.
- The interpretability is further enhanced by using ChatGPT to transform technical explanations given by the model into human-readable commentaries.
BERT-XDD introduces an effective and modular approach for interpretable depression detection, which can foster the development of more socially responsible digital platforms, facilitating early intervention and support for mental health challenges under the guidance of qualified healthcare professionals.
Belcastro, L., Cantini, R., Marozzo, F., Talia, D., & Trunfio, P. (2024). Detecting mental disorder on social media: a ChatGPT-augmented explainable approach. arXiv preprint arXiv:2401.17477.
This repository hosts all the code (Jupyter notebooks) and data necessary for reproducing experiments. The dataset utilized (included within this repository) was originally provided by Rafał Poświata et al., and can also be accessed at the following link: https://github.com/rafalposwiata/depression-detection-lt-edi-2022