Su-RoBERTa: A Semi-supervised Approach to Predicting Suicide Risk through Social Media using Base Language Models

TL;DR

This paper introduces Su-RoBERTa, a semi-supervised model fine-tuned on social media data, demonstrating that smaller language models can effectively predict suicide risk with an efficient approach.

Contribution

The study presents Su-RoBERTa, a novel semi-supervised fine-tuning method using base language models and data augmentation for suicide risk prediction from social media.

Findings

Su-RoBERTa achieved a 69.84% weighted F1 score.

Smaller language models (<500M parameters) are effective for this task.

Data augmentation with GPT-2 improves class imbalance handling.

Abstract

In recent times, more and more people are posting about their mental states across various social media platforms. Leveraging this data, AI-based systems can be developed that help in assessing the mental health of individuals, such as suicide risk. This paper is a study done on suicidal risk assessments using Reddit data leveraging Base language models to identify patterns from social media posts. We have demonstrated that using smaller language models, i.e., less than 500M parameters, can also be effective in contrast to LLMs with greater than 500M parameters. We propose Su-RoBERTa, a fine-tuned RoBERTa on suicide risk prediction task that utilized both the labeled and unlabeled Reddit data and tackled class imbalance by data augmentation using GPT-2 model. Our Su-RoBERTa model attained a 69.84% weighted F1 score during the Final evaluation. This paper demonstrates the effectiveness of Base language models for the analysis of the risk factors related to mental health with an efficient computation pipeline