Privacy-Conscious Internet Behavior for Depression Detection with Cross-Scale Adaptive Transformer

Depression remains a leading cause of suicide among college students, highlighting the need for effective and scalable screening methods. Internet usage behavior has shown strong potential for identifying depressive tendencies, but privacy concerns limit its practical use. In this study, we propose a privacy-conscious cross-scale adaptive transformer designed for irregular time series data derived from weakly private online behavior, such as application categories and usage patterns, while excluding content-sensitive or personally identifiable information. Our model incorporates an adaptive sampling strategy to unify temporal resolutions and uses a cross-scale attention mechanism to capture depression-related behavioral patterns. We compared several classic models for irregular time series data, and the proposed method outperformed them, offering a promising, non-intrusive approach for depression detection based on privacy-conscious online activity patterns.