Detection of Suicidal Risk on Social Media: A Hybrid Model

TL;DR

This paper presents a hybrid machine learning model combining RoBERTa, TF-IDF, and PCA to classify Reddit posts into four levels of suicidal risk, improving accuracy for early detection.

Contribution

It introduces a novel hybrid model integrating deep contextual embeddings with statistical features for multi-class suicide risk classification.

Findings

Hybrid model achieves a weighted F1 score of 0.7512.

Data resampling and augmentation improve model generalization.

Compared with RoBERTa alone and traditional classifiers, the hybrid model performs better.

Abstract

Suicidal thoughts and behaviors are increasingly recognized as a critical societal concern, highlighting the urgent need for effective tools to enable early detection of suicidal risk. In this work, we develop robust machine learning models that leverage Reddit posts to automatically classify them into four distinct levels of suicide risk severity. We frame this as a multi-class classification task and propose a RoBERTa-TF-IDF-PCA Hybrid model, integrating the deep contextual embeddings from Robustly Optimized BERT Approach (RoBERTa), a state-of-the-art deep learning transformer model, with the statistical term-weighting of TF-IDF, further compressed with PCA, to boost the accuracy and reliability of suicide risk assessment. To address data imbalance and overfitting, we explore various data resampling techniques and data augmentation strategies to enhance model generalization. Additionally, we compare our model's performance against that of using RoBERTa only, the BERT model and other traditional machine learning classifiers. Experimental results demonstrate that the hybrid model can achieve improved performance, giving a best weighted $F_{1}$ score of 0.7512.