Hijacking online reviews: sparse manipulation and behavioral buffering in popularity-biased rating systems

ArXi:2604.13049v1 Announce Type: cross Online reviews and recommendation systems help users navigate overwhelming choice, but they are vulnerable to self-reinforcing distortions. This paper examines how a single malicious reviewer can exploit popularity-biased rating dynamics and whether behavioral heterogeneity in user responses can reduce the damage. We develop a minimal agent-based model in which users choose what to rate partly on the basis of currently displayed averages.