Abstract
In this article, we introduce a new method for analyzing and summarizing posts from r/SuicideWatch on Reddit, overcoming the limitations of current techniques in processing complex mental health discussions online. Existing methods often struggle to accurately identify and contextualize subtle expressions of mental health problems, leading to inadequate support and intervention strategies. Our approach combines the open-source Large Language Model (LLM), fine-tuned with health-oriented knowledge, to effectively process Reddit posts. We also design prompts that focus on suicide-related statements, extracting key statements, and generating concise summaries that capture the core aspects of the discussions. The preliminary results indicate that our method improves the understanding of online suicide-related posts compared to existing methodologies.- Anthology ID:
- 2024.clpsych-1.23
- 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:
- 238–246
- Language:
- URL:
- https://aclanthology.org/2024.clpsych-1.23
- DOI:
- Cite (ACL):
- Jingwei Zhu, Ancheng Xu, Minghuan Tan, and Min Yang. 2024. XinHai@CLPsych 2024 Shared Task: Prompting Healthcare-oriented LLMs for Evidence Highlighting in Posts with Suicide Risk. In Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024), pages 238–246, St. Julians, Malta. Association for Computational Linguistics.
- Cite (Informal):
- XinHai@CLPsych 2024 Shared Task: Prompting Healthcare-oriented LLMs for Evidence Highlighting in Posts with Suicide Risk (Zhu et al., CLPsych-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.clpsych-1.23.pdf