Psychological Profiling Using Digital Behavior Signals

Abstract

Digital behavior signals—ranging from browsing patterns, language use, interaction frequency, biometric metadata, and online social network structures—have enabled computational models to infer psychological traits and behavioral tendencies. While these predictive systems advance research in personalized learning, mental health screening, and user experience optimization, they raise concerns around privacy, consent, algorithmic bias, and potential misuse. This paper synthesizes interdisciplinary literature from computational psychology, data science, digital sociology, and ethics to examine how behavioral signals correlate with psychological constructs. A new model, the Digital Behavioral Profiling Framework (DBPF), is proposed to conceptualize ethical analysis, data interpretation, and safeguards. The paper explicitly focuses on ethical, privacy-aligned, and non-exploitative applications.