HIT-SCIR@eRisk2025: Exploring the Potential of a Learnable Screening Model and Risk Post Buffer-Based Framework for Contextualized Early Prediction of Depression on Social Media

Task 2 of the eRisk lab at CLEF 2025 focuses on the contextualized early detection of depression using user posts from Reddit. The HIT-SCIR team participate in this task, submitting five runs based on different configurations of our proposed Learnable Screening Model and Risk Post Buffer-Based Framework. Our approach involves several key components: contextual data augmentation using Large Language Models (LLMs) to simulate social interactions and generate summaries for training data; a core end-to-end learnable risky post screening model guided by symptom descriptions from established psychiatric scales; and a depression risk detector utilizing MentalBERT for classification. The official results on the test data demonstrate that our framework ranked first across several evaluation metrics, notably F1-score, ERDE50, Flatency, and various ranking-based measures. This note describes the architecture, experimental setup, and performance analysis of our system, highlighting the value of integrating psychiatric knowledge into a learnable, context-aware model.