Rewarding Engagement and Personalization in Popularity-Based Rankings Amplifies Extremism and Polarization

Despite extensive research, the mechanisms through which online platforms shape extremism and polarization remain poorly understood, largely because observational settings make it difficult to identify causal effects. We propose a framework that analyses a specific mechanism: the feedback loop between popularity-based rankings and user behavior (e.g., partisan sorting and attention dynamics), and its consequences for content prominence. Our framework formalizes this feedback loop using a dynamical, popularitybased ranking model with a small set of interpretable parameters, and evaluates it through simulations and interactive experiments with hundreds of human participants. When trained on real-world data, the model reproduces well-established interaction patterns with ranked content: (1) users exhibit position bias, (2) prefer likeminded content, and (3) more extreme users engage more actively. Using this framework, we show that when platforms reward active engagement and implement personalized rankings, users are systematically driven toward increasingly extremist and polarized news consumption across a wide range of parameter settings.

• Human-centered computing → Empirical studies in collaborative and social computing.