Selective Exposure to News, Homogeneous Political Discussion Networks, and Affective Political Polarization: An Agent-Based Modeling of Minimal versus Strong Communication Effects

This study employs an agent-based model (ABM) simulation grounded in polarization theory to explore how social and media influences shape affective polarization, social diversity, and media diversity within a hostile partisan information environment. We examine four scenarios: minimal, strong, social-dominated effect, and media-dominated effect, by iterating 16 combinations of parameters, including influence strength, homogeneous discussion, and selective exposure rates. Calibrated with a real-world U.S. population dataset, results show that while social and media influences accelerate sorting, selectivity structure is the primary driver of affective polarization. Notably, low homogeneous discussion and low selective exposure produce the highest polarization levels across four scenarios due to a probabilistic backfire effect that reflects identity-protective cognition. Strong, locally concentrated social influence sharply reduces social diversity, whereas media influence alone cannot produce convergence without social reinforcement. Media diversity proves more resilient due to global exposure, though it declines under high selective exposure and strong media influence. Despite initial partisan gaps, final-stage outcomes reveal minimal differences between Democrats and Republicans across all conditions.