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Improving Suicidal Ideation Detection in Social Media Posts: Topic Modeling and Synthetic Data Augmentation Approach.

Hamideh Ghanadian¹, Isar Nejadgholi², Hussein Al Osman¹

¹School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada.

JMIR Formative Research

|June 11, 2025

Summary

This summary is machine-generated.

Synthetic data generation improves AI models for detecting online suicide discussions, especially for underrepresented groups. This approach enhances topic diversity and accuracy in identifying critical risk factors in social media conversations.

Keywords:

large language models social media suicidal ideation detection synthetic data topic modeling

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Area of Science:

Computational Social Science
Artificial Intelligence
Public Health Informatics

Background:

Online suicide discussions are crucial for public health but often lack representation of marginalized communities.
Underrepresentation in social media data leads to underperformance of AI models on critical topics.

Purpose of the Study:

To analyze online suicide discussions and identify underrepresented topics.
To generate synthetic data to improve the diversity and coverage of suicide-related datasets for AI training.

Main Methods:

Unsupervised and guided topic modeling on social media data.
Scoping review of psychology literature for suicide risk factors.
Generative large language models (GPT-3.5 Turbo) for synthetic data creation.
Evaluation of synthetic data for readability, complexity, and utility.

Main Results:

Critical topics, especially those concerning marginalized communities and racism, were underrepresented in real social media data.
Synthetic data generation improved topic diversity and augmented datasets.
Fine-tuning AI classifiers with augmented data significantly improved suicidal ideation detection accuracy (F1-score from 0.70 to 0.90).

Conclusions:

Synthetic datasets are valuable for understanding online suicide discourse.
AI models trained with synthetic data augmentation show improved accuracy in detecting suicidal narratives.