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.

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的形成。尽管算法同质化现象已有充分记录，但基于内容与基于链接的推荐策略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），本研究发现：与基于链接的算法不同，基于内容的算法会将社交网络导向"隔离先于极化"（SbP）的演化路径。在此轨迹中，结构隔离先于观点分化，既加速了个体孤立，又延迟但最终加剧了集体极化。此外，研究揭示了信息共享的悖论：转发行为虽能增加网络连接数量，却同时强化了回音室效应——因为它放大了原本微不足道的潜在观点差异。这些发现表明，缓解极化需要分阶段的算法干预策略，即随着网络演化从以内容为中心转向以结构为中心的治理路径。