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

TL;DR

This paper demonstrates how popularity-based, personalized ranking algorithms that reward engagement can unintentionally promote extremism and polarization online, supported by a formal model and human experiments.

Contribution

It introduces a formal dynamical model explaining how engagement rewards and personalization amplify extremism and polarization in online rankings.

Findings

Ranking algorithms can unintentionally promote extremism.

Personalized, engagement-rewarding rankings increase polarization.

Simulations and experiments support the proposed mechanism.

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.