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.

翻译：社交媒体平台通过用户偏好与推荐算法之间的反馈循环助长了回音室效应。尽管算法同质性已有充分文献记载，但基于内容与基于链接的推荐所驱动的不同演化路径仍不明确。通过扩展的动态有界置信模型（BCM），我们证明基于内容的算法——与基于链接的算法不同——引导社交网络走向"极化前先隔离"（SbP）路径。沿着这一轨迹，结构上的隔离先于观点分歧发生，加速了个人孤立，同时延迟但最终加剧了集体极化。此外，我们揭示出转发行为看似通过将内容传播至直接关注链接之外而具有连接性，但实则强化了回音室效应——因为它放大了原本无关紧要的微小潜在观点差异。这些发现表明，缓解极化需要因阶段而异的算法干预，随着网络演化，应从以内容为中心的策略转向以结构为中心的策略。