Online Homogeneity Can Emerge Without Filtering Algorithms or Homophily Preferences

2025-08-14

AI Summary

This study challenges the dominant attribution of online ideological homophily to algorithmic filtering or preexisting user preferences. Using an agent-based simulation framework extending Schelling’s model, we examine dynamic user exit and community reformation in the absence of algorithmic curation and without ex ante homophily preferences. Results demonstrate that even weak individual preferences, coupled with network interaction structure, can initiate self-reinforcing feedback loops—leading to systemic homophily and community polarization. Crucially, homophily emerges spontaneously as a system-level emergent phenomenon in digital public spheres; under certain conditions, algorithmic filtering may actually mitigate polarization and preserve diversity. These findings reconceptualize causal attributions for online polarization, highlighting the critical role of structure–behavior coevolution. The work provides a novel theoretical foundation for platform governance, emphasizing structural affordances over individual or algorithmic determinism. (149 words)

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Application Domains: Humanities & Computational Social Science
• Knowledge Representation and Reasoning: Preferences

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Social networks and social learning

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.

Problem

• Explores emergence of online homogeneity without algorithms or preferences
• Challenges view that homophily stems from algorithms or user choices
• Shows weak preferences and group structures drive segregation dynamics

Innovation

• Agent-based model simulates online homogeneity emergence
• Weak preferences and group interactions trigger feedback loops
• Algorithmic filtering can sustain diversity in communities