A Time-Dependent-Based Approach to Enhance Self-Harm Prediction

Abstract

We present a time-dependent approach for learning potential features that may explain the early risk of human self-harm. Rather than only extracting features from text posted by users, as suggested by several approaches, we propose remodeling the user posts into sequential data. We demonstrate that the sequences reflecting the longitudinal grammatical language of users allow the improved performance of classification algorithms in predicting self-harm behavior. The experimental results on the eRisk 2019 data corroborate our claim.