Abstract
Due to the rapid growth of user interaction on different social media platforms, publicly available social media data has increased substantially. The sheer amount of data and level of personal information being shared on such platforms has made analyzing textual information to predict mental disorders such as depression a reliable preliminary step when it comes to psychometrics. In this study, we first proposed a system to search for texts that are related to depression symptoms from the Beck’s Depression Inventory (BDI) questionnaire, and providing a ranking for further investigation in a second step. Then, in this second step, we address the even more challenging task of automatic depression level detection, using writings and voluntary answers provided by users on Reddit. Several Large Language Models (LLMs) were applied in experiments. Our proposed system based on LLMs can generate both predictions and explanations for each question. By combining two LLMs for different questions, we achieved better performance on three of four metrics compared to the state-of-the-art and remained competitive on the one remaining metric. In addition, our system is explainable on two levels: first, knowing the answers to the BDI questions provides clues about the possible symptoms that could lead to a clinical diagnosis of depression; second, our system can explain the predicted answer for each question.- Anthology ID:
- 2024.clpsych-1.8
- Volume:
- Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
- Month:
- March
- Year:
- 2024
- Address:
- St. Julians, Malta
- Editors:
- Andrew Yates, Bart Desmet, Emily Prud’hommeaux, Ayah Zirikly, Steven Bedrick, Sean MacAvaney, Kfir Bar, Molly Ireland, Yaakov Ophir
- Venues:
- CLPsych | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 108–126
- Language:
- URL:
- https://aclanthology.org/2024.clpsych-1.8
- DOI:
- Cite (ACL):
- Yuxi Wang, Diana Inkpen, and Prasadith Kirinde Gamaarachchige. 2024. Explainable Depression Detection Using Large Language Models on Social Media Data. In Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024), pages 108–126, St. Julians, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Explainable Depression Detection Using Large Language Models on Social Media Data (Wang et al., CLPsych-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.clpsych-1.8.pdf