Segregation Before Polarization: How Recommendation Strategies Shape Echo Chamber Pathways
For social media platforms and researchers studying echo chambers, this work identifies distinct evolutionary pathways driven by different recommendation strategies, highlighting the need for stage-dependent interventions.
This paper shows that content-based recommendation algorithms drive social networks toward a segregation-before-polarization pathway, where structural segregation precedes opinion divergence, accelerating individual isolation while delaying but intensifying collective polarization. Reposting amplifies latent opinion differences, reinforcing echo chambers.
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 that reposting appears connective by circulating content beyond direct follow links, 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.