Emotion as a cross-layer mechanism in filter bubbles: a social-psychological perspective

In increasingly personalized media environments, individuals encounter information that aligns with their existing beliefs, raising concerns about polarization, intergroup hostility, and the erosion of shared political reality. This review synthesizes recent research from a social-psychological perspective, arguing that filter bubbles are not produced by algorithms alone but emerge through the recursive interaction of motivated cognitive processing, identity-based social network structures, and algorithmic amplification of behavioral and emotional cues. We identify emotion as an underrecognized yet central mechanism that operates across these layers: emotional states such as anger, threat, and defiant self-worth guide information seeking, reinforce group affiliation, and shape algorithmic recommendation patterns, thereby intensifying filtering dynamics and contributing to attitude extremization. By conceptualizing filter bubbles as systems of cognitive coherence, identity protection, and affective regulation, we propose a dynamic multi-level explanatory model and outline implications for interventions, including reflective reasoning strategies, weak-tie exposure, and approaches that address the emotional and identity foundations of information selection.