Segregation Before Polarization: How Recommendation Strategies Shape Echo Chamber Pathways

TL;DR

This paper demonstrates how different recommendation strategies on social media influence the development of echo chambers, showing that content-based algorithms lead to early structural segregation before opinion polarization, with reposting reinforcing echo chambers.

Contribution

It introduces an extended dynamic BCM to distinguish how content-based and link-based recommendations differently shape echo chamber pathways, highlighting the stage-dependent effects on social network evolution.

Findings

Content-based algorithms cause early structural segregation before polarization.

Reposting increases connections but reinforces echo chambers.

Mitigating polarization requires stage-specific algorithmic interventions.

Abstract

Social media platforms facilitate echo chambers through feedback loops between user preferences and recommendation algorithms. While algorithmic homogeneity is well-documented, the distinct evolutionary pathways driven by content-based versus link-based recommendations remain unclear. Using an extended dynamic Bounded Confidence Model (BCM), we show that content-based algorithms--unlike their link-based counterparts--steer social networks toward a segregation-before-polarization (SbP) pathway. Along this trajectory, structural segregation precedes opinion divergence, accelerating individual isolation while delaying but ultimately intensifying collective polarization. Furthermore, we reveal a paradox in information sharing: Reposting increases the number of connections in the network, yet it simultaneously reinforces echo chambers because it amplifies small, latent opinion differences that would otherwise remain inconsequential. These findings suggest that mitigating polarization requires stage-dependent algorithmic interventions, shifting from content-centric to structure-centric strategies as networks evolve.