Leveraging Text Analysis and Machine Learning Models for Suicide Ideation Detection in Social Media Posts

Abstract

The escalating global suicide rates underscore the urgency to leverage social media platforms for mental health discourse. However, discerning suicidal content amidst mental health discussions poses challenges, given the nuanced progression from mental health disorders to suicidal ideation. This study addresses this issue by analyzing text related to suicide ideation within social media posts. Utilizing a dataset featuring text labels for suicide and non-suicide sourced from the SuicideWatch and Depression subreddits on Kaggle, we employ common word and bigram analysis to uncover patterns and relationships. Additionally, emotion analysis is conducted to discern underlying emotional tones in the text corpus. Machine learning and deep learning models are deployed for suicide ideation classification. Support Vector Machines (SVM) coupled with Universal Sentence Encoder (USE) word embedding achieved the highest performance among machine learning models, yielding an accuracy of 94.8% and an F1-score of 0.948. Notably, the deep learning model MentalBERT outperformed others, attaining an accuracy of 97.7% and an F1-score of 0.98. These findings hold promise for assisting professionals in timely identification and provision of mental health support to individuals exhibiting suicidal tendencies. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.