Extraction and summarization of suicidal ideation evidence in social media content using large language models

Loitongbam, Gyanendro, Mao, Junyu, Mutalik, Rudra and Middleton, Stuart E. (2024) Extraction and summarization of suicidal ideation evidence in social media content using large language models. The Ninth Workshop on Computational Linguistics and Clinical Psychology, Radisson Blu, St Julian's, Malta. 21 Mar 2024.

Record type: Conference or Workshop Item (Paper)

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.

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