Application of Generative AI to Suicidal Ideation on Social Media

Background
Computational approaches to suicide risk detection using social media have grown rapidly in recent years. However, much of existing literature relies on oversimplified binary classification frameworks. Additionally, many approaches depend on machine learning models, which struggle to capture semantic nuance in short, informal social media text.

Objective
This study applies generative AI to (1) develop a multi-class framework that distinguishes genuine suicidal ideation from context-absent suicidal statements and exaggerated expressions on Twitter, and (2) compare longitudinal risk profiles across these categories.

Methods
Using a Twitter dataset from 2016 to 2020, approximately 47 million tweets were screened using suicidal keywords, resulting in 3,807 candidate tweets for analysis. The ChatGPT model was applied to classify tweets into 4 categories: genuine suicidal ideation, context-absent suicidal statements, exaggerated expressions, and no suicidal ideation. Model performance was validated against human annotation. Then, up to 6 months of historical tweets preceding the index tweet, comprising of 417,000 tweets, were analyzed using a two-stage ChatGPT-based pipeline to identify suicide risk signals. The OpenAI batch API was used to perform large-scale classification. Group differences in proportional risk-factor burden were assessed using non-parametric Kruskal-Wallis tests.

Results
The AI classifier achieved a weighted F1-score of 0.95. Users expressing genuine suicidal ideation exhibited significantly higher levels of depression, hopelessness, diagnosed psychological disorders, prior suicidal ideation, and self-harm intent compared with users engaging in exaggerated statements. Several risk factors, such as loneliness and negative self-concept, were observed at similar levels across the groups.

Conclusion
These findings demonstrate that the AI-driven framework produces internally coherent groupings that align with established psychosocial risk profiles associated with suicidal ideation.

Generative AI

Suicidal Ideation

Twitter 

AI and LLM Applications

Symposium on Data Science and Statistics (SDSS) 2026