Tracing the Emotional Roadmap of Depressive Users on Social Media through Sequential Pattern Mining

Abstract

Depression is one of the most growing health disorders, generating social and economic problems. The affective computing models focus on analyzing unique user posts, not observing temporal behavior patterns, which are essential to track changes and the evolution of emotional behavior and user context, that involves the persistent analysis of feelings and characteristics over time. This article proposes the TROAD framework for longitudinal recognition of sequential patterns from depressive users on social media. The framework identifies the best interval to analyze every user activity, extracts emotional and contextual features from user data, and models the features into time windows to recognize sequential patterns from depressive user behavior. The main characteristics of the users found in the top-10 rules are negative emotions: violence, pain, shame, depression, sadness, and silence. We obtained strong sequence patterns with a minimum of 70% of support, 81% of confidence, and 69% regarding sequential confidence, considering periods of silence between users' posts. Without considering silent periods, the rules showed 70%, 86%, and 38% of support, confidence, and sequential confidence. TROAD computational approach is a promising tool for clinical specialists in human behavior.