Work

Leveraging timeline integration methods for effective suicidal ideation detection

Year: 2025

Type: article

Abstract: Suicidal Ideation Detection (SID) is a critical task that involves analyzing a user’s social media posts to assess the severity of suicidal ideation (SI) and enable early intervention. Recent research has leveraged the advanced language understanding capabilities of large language models (LLMs) to perform this task more effectively. However, existing methods often process multiple posts simultaneously, which may overlook temporal dependencies between posts and exceed input length limitations. To address these challenges, we introduce timeline integration methods (TIMs), which model temporal dependencies across posts by incorporating information from earlier time points through prompt components such as the predicted label distribution of previous posts or recent post texts. This approach enables LLMs to track the progression of SI risk and make more accurate severity assessments. Furthermore, it remains unclear whether domain-specific LLMs for the medical field outperform general-purpose LLMs in SID tasks. We apply TIMs across various LLMs and benchmark them against existing methods. Our key findings indicate that: (1) TIMs significantly improve the performance of LLMs on the SID task, particularly on long user posts; (2) the effectiveness of TIMs varies depending on the model size and its capability in reading comprehension; and (3) domain-specific LLMs for the medical field show superior performance in SID, particularly when enhanced with TIMs.

Source: Expert Systems with Applications

DOI: 10.1016/j.eswa.2025.130190

Authors: Seulgi Kim, Seulgi Kim, Jaewook Lee, Kwangil Kim, Kwangil Kim, Harksoo Kim

Institution: Konkuk University

Language: English

Topic: Mental Health via Writing

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,500