Innovative Use of Self-Attention-Based Ensemble Deep Learning for Suicide Risk Detection in Social Media Posts

Abstract

Suicidal ideation constitutes a critical concern in mental health, adversely affecting individuals and society at large. The early detection of such ideation is vital for providing timely support to individuals and mitigating its societal impact. With social media serving as a platform for self-expression, it offers a rich source of data that can reveal early symptoms of mental health issues. This paper introduces an innovative ensemble learning method named LSTM-Attention-BiTCN, which fuses LSTM and BiTCN models with a self-attention mechanism to detect signs of suicidality in social media posts. Our LSTM-Attention-BiTCN model demonstrated superior performance in comparison to baseline models in the realm of classification and suicidal ideation detection, boasting an accuracy of 0.9405, a precision of 0.9385, a recall of 0.9424, and an F1-score of 0.9405. Our proposed model can aid healthcare professionals in recognizing suicidal tendencies among social media users accurately, thereby contributing to efforts to reduce suicide rates.