Characterizing retroactive jealousy: Evidence from network, machine learning, and qualitative approaches

Abstract

Retroactive jealousy is a distressing and poorly understood phenomenon characterized by intrusive thoughts, compulsive behaviors, and emotional distress in response to a romantic partner’s past relationships. This dissertation aims to advance understanding of retroactive jealousy through a series of empirical investigations spanning network analysis, machine learning, and computational linguistic approaches. In Study 1, we examine the symptom structure of retroactive jealousy and its relationship to obsessive-compulsive disorder (OCD), relationship OCD (ROCD), and borderline personality disorder (BPD). We find that retroactive jealousy symptoms do not form a strongly interconnected network, suggesting that the condition may not represent a coherent syndrome. In Study 2, we apply latent profile analysis to identify potential subtypes of retroactive jealousy based on behavioral strategies. Results reveal subtle differences in coping styles but limited evidence for stable or clinically distinct subtypes. In Study 3, we explore cognitive mechanisms associated with retroactive jealousy using open-ended responses analyzed through computational linguistics. We find that higher retroactive jealousy severity is associated with lower self-reported cognitive flexibility and greater use of certainty-related language, suggesting a role for rigid cognitive schemas. Drawing on work in the philosophy of mental illness, we also consider the possibility that retroactive jealousy reflects a historically and culturally situated expression of distress shaped by modern romantic ideals and digital media. Together, these studies suggest that retroactive jealousy may not be best conceptualized as a form of an existing disorder category, but rather as a culturally embedded cognitive-affective experience that raises broader questions about the nature of emergent psychopathology.