Extracting and Summarizing Evidence of Suicidal Ideation in Social Media Contents Using Large Language Models

Loitongbam Gyanendro Singh, Junyu Mao, Rudra Mutalik, Stuart E. Middleton


Abstract
This paper explores the use of Large Language Models (LLMs) in analyzing social media content for mental health monitoring, specifically focusing on detecting and summarizing evidence of suicidal ideation. We utilized LLMs Mixtral7bx8 and Tulu-2-DPO-70B, applying diverse prompting strategies for effective content extraction and summarization. Our methodology included detailed analysis through Few-shot and Zero-shot learning, evaluating the ability of Chain-of-Thought and Direct prompting strategies. The study achieved notable success in the CLPsych 2024 shared task (ranked top for the evidence extraction task and second for the summarization task), demonstrating the potential of LLMs in mental health interventions and setting a precedent for future research in digital mental health monitoring.
Anthology ID:
2024.clpsych-1.20
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:
218–226
Language:
URL:
https://aclanthology.org/2024.clpsych-1.20
DOI:
Bibkey:
Cite (ACL):
Loitongbam Gyanendro Singh, Junyu Mao, Rudra Mutalik, and Stuart E. Middleton. 2024. Extracting and Summarizing Evidence of Suicidal Ideation in Social Media Contents Using Large Language Models. In Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024), pages 218–226, St. Julians, Malta. Association for Computational Linguistics.
Cite (Informal):
Extracting and Summarizing Evidence of Suicidal Ideation in Social Media Contents Using Large Language Models (Gyanendro Singh et al., CLPsych-WS 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.clpsych-1.20.pdf
Video:
 https://aclanthology.org/2024.clpsych-1.20.mp4