Transformer-Based Ensemble Learning for Symptom-Level Classification and DSM-5-Oriented Depression Screening on Social Media

Depression screening from social media has increasingly benefited from transformer-based architectures; however, integrating symptom-level analysis with clinically grounded diagnostic screening remains challenging. This study proposes a unified two-phase framework for social media-based depression detection aligned with DSM-5 criteria. In Phase 1, transformer-based learning strategies—Single, Voting, Stacking, Bagging, and Boosting—are employed to perform symptom-level multi-class classification of depressive symptoms. In Phase 2, the predicted symptoms are aggregated over a 14-day observation window to enable DSM-5-oriented binary depression screening. To ensure a robust and consistent evaluation, eight preprocessing configurations (D1–D8) are incorporated into the framework. Experimental results demonstrate that Bagging achieves the highest performance in symptom-level classification (F1 = 0.9394), while Voting and Boosting yield superior performance in DSM-5-oriented screening (F1-Yes = 0.7273). The findings reveal that different learning mechanisms play distinct roles across diagnostic levels, with variance-reduction strategies enhancing symptom differentiation and consensus-based approaches improving recall in clinical screening. This study provides a structured and clinically aligned framework for social media-based depression detection, offering practical insights for developing robust and scalable mental health screening systems.