内容推荐导致先隔离后极化，加剧信息茧房
Segregation Before Polarization: How Recommendation Strategies Shape Echo Chamber Pathways
- 用扩展的有界信心模型分析推荐机制演化路径
- 内容推荐引发先分离后极化的结构变化，加速个体孤立
- 转发虽看似连接却强化隐性意见差异，适合政策与算法设计者阅读
社交媒体平台通过用户偏好与推荐算法之间的反馈回路促成信息茧房。尽管算法同质性已被广泛记录，但基于内容与基于链接的推荐机制所驱动的不同演化路径仍不明确。本文使用扩展的动态有界信心模型（Extended Dynamic Bounded Confidence Model, BCM）发现，内容推荐算法——不同于基于链接的推荐——引导社交网络走向‘先隔离后极化’（Segregation-before-Polarization, SbP）路径。在此路径中，结构上的隔离先于观点分歧出现，加速个体孤立，同时延迟但最终加剧集体极化。此外，我们揭示转发行为虽看似扩大内容传播范围，突破直接关注关系，却同时强化信息茧房，因其放大原本微小且潜在的意见差异，使其产生显著影响。研究建议：缓解极化需阶段性干预，随网络演化从以内容为中心转向以结构为中心的策略。
原文摘要 · Abstract (English)
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.
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