Online Homogeneity Can Emerge Without Filtering Algorithms or Homophily Preferences

TL;DR

This paper shows that online ideological homogeneity can arise without filtering algorithms or user preferences, driven instead by simple interaction structures and feedback loops, with implications for understanding polarization.

Contribution

It demonstrates that community segregation and homogeneity can emerge from basic interaction rules, challenging the idea that algorithms or preferences are necessary for echo chambers.

Findings

Homogeneity can emerge without filtering algorithms or homophily preferences.

Simple group interactions can trigger feedback loops leading to segregation.

Algorithmic filtering can actually help maintain diversity.

Abstract

Ideologically homogeneous online environments - often described as "echo chambers" or "filter bubbles" - are widely seen as drivers of polarization, radicalization, and misinformation. A central debate asks whether such homophily stems primarily from algorithmic curation or users' preference for like-minded peers. This study challenges that view by showing that homogeneity can emerge in the absence of both filtering algorithms and user preferences. Using an agent-based model inspired by Schelling's model of residential segregation, we demonstrate that weak individual preferences, combined with simple group-based interaction structures, can trigger feedback loops that drive communities toward segregation. Once a small imbalance forms, cascades of user exits and regrouping amplify homogeneity across the system. Counterintuitively, algorithmic filtering - often blamed for "filter bubbles" - can in fact sustain diversity by stabilizing mixed communities. These findings highlight online polarization as an emergent system-level dynamic and underscore the importance of applying a complexity lens to the study of digital public spheres.