Echo chambers can emerge without algorithmic personalization or a preference for homogeneity.

Area of Science:

• Computational Social Science
• Sociology
• Network Science

Background:

• Online ideological segregation, or "echo chambers," is often blamed on algorithmic personalization (filter bubbles) or user preference for like-minded groups.
• Existing theories do not fully explain the emergence of segregation in online environments.

Purpose of the Study:

• To propose and test a novel mechanism for online ideological segregation.
• To investigate the role of user exit dynamics in creating and reinforcing echo chambers.
• To explore how algorithmic personalization interacts with user behavior to influence segregation.

Main Methods:

• Development of a minimal agent-based model to simulate user interactions and community exits.
• Analysis of cascading exit dynamics and self-reinforcing sorting processes.
• Longitudinal analysis of user language and exit behavior in the subreddit r/MensRights as an empirical case study.

Main Results:

• Strong ideological segregation can emerge organically from user exit dynamics, even without algorithmic personalization or explicit preference for homogeneity.
• Cascading exits, triggered by encountering disagreement, can lead to highly homogeneous communities.
• Algorithmic personalization can, under certain conditions, reduce segregation by mitigating user dissatisfaction and stabilizing mixed communities.
• Empirical data from r/MensRights supports the model, showing users with language distant from the community center are more likely to exit.

Conclusions:

• Online echo chambers can arise from the inherent dynamics of user exits and regrouping, independent of personalization or homophily.
• Interventions targeting individual exposure may have unintended aggregate consequences on polarization.
• Understanding these exit dynamics is crucial for addressing online polarization and informing platform design and policy.