Rewarding Engagement and Personalization in Popularity-Based Rankings Amplifies Extremism and Polarization
Abstract
Despite extensive research, the mechanisms through which online platforms shape extremism and polarization remain poorly understood. We identify and test a mechanism, grounded in empirical evidence, that explains how ranking algorithms can amplify both phenomena. This mechanism is based on well-documented assumptions: (i) users exhibit position bias and tend to prefer items displayed higher in the ranking, (ii) users prefer like-minded content, (iii) users with more extreme views are more likely to engage actively, and (iv) ranking algorithms are popularity-based, assigning higher positions to items that attract more clicks. Under these conditions, when platforms additionally reward \emph{active} engagement and implement \emph{personalized} rankings, users are inevitably driven toward more extremist and polarized news consumption. We formalize this mechanism in a dynamical model, which we evaluate by means of simulations and interactive experiments with hundreds of human participants, where the rankings are updated dynamically in response to user activity.
Cite
@article{arxiv.2510.24354,
title = {Rewarding Engagement and Personalization in Popularity-Based Rankings Amplifies Extremism and Polarization},
author = {Jacopo D'Ignazi and Emma Fraxanet Morales and Andreas Kaltenbrunner and Gaël Le Mens and Fabrizio Germano and Vicenç Gómez},
journal= {arXiv preprint arXiv:2510.24354},
year = {2026}
}