WET -- Weighted Ensemble Transformer for Identifying Psychiatric Stressors Related to Suicide on X (formerly Twitter)
Ali Sahandi, Mahsa Pahlavan Yousefkhani, Mehrshad Eisaei, Hossein Momeni, Ramin Mousa

TL;DR
This paper introduces WET, a dual-branch deep learning model combining Transformer-based semantic analysis with emotional and behavioral features to improve detection of suicide-related stressors on social media.
Contribution
The paper presents a novel Weighted Ensemble Transformer architecture that integrates semantic and auxiliary signals for better suicide risk detection on X.
Findings
WET achieves 0.9901 accuracy in binary classification.
Hybrid approach outperforms traditional machine learning and baseline transformers.
Incorporating emotional and behavioral features significantly enhances detection performance.
Abstract
Suicide remains one of the leading causes of death worldwide, particularly among young people, and psychological stressors are consistently identified as proximal drivers of suicidal ideation and behavior. In recent years, social media platforms such as X have become critical environments where individuals openly disclose emotional distress and conditions associated with suicidality, creating new opportunities for early detection and intervention. Existing approaches, however, predominantly rely on raw textual content and often neglect auxiliary emotional and contextual signals embedded in user metadata. To address this limitation, we propose a Weighted Ensemble Transformer (WET), a dual branch deep learning architecture designed to identify psychiatric stressors associated with suicide in X posts. Our model integrates semantic representations extracted through Transformer encoders with…
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