Neuro-Symbolic Integration of Graph Attention Networks and Knowledge Graphs for Interpretable Personality Prediction from Digital Footprints

Abstract

This paper addresses the problem of interpretable personality prediction from social media digital footprints. While modern deep learning models demonstrate high accuracy in personality assessment tasks, their black-box nature limits their applicability in psychologically and socially sensitive domains. To overcome this limitation, a neuro-symbolic approach is proposed that combines neural feature extraction from textual and visual data with reasoning over a domain-specific knowledge graph. The architecture integrates Graph Attention Networks to model relationships between observable digital behaviors and personality traits defined by Cattell’s 16 Personality Factors. The study evaluates the proposed approach on social media profiles with psychometric annotations and demonstrates that the model provides competitive predictive performance while enabling transparent and psychologically meaningful explanations.