News recommender systems play an increasingly influential role in shaping
information access within democratic societies. However, tailoring
recommendations to users' specific interests can result in the divergence of
information streams. Fragmented access to information poses challenges to the
integrity of the public sphere, thereby influencing democracy and public
discourse. The Fragmentation metric quantifies the degree of fragmentation of
information streams in news recommendations. Accurate measurement of this
metric requires the application of Natural Language Processing (NLP) to
identify distinct news events, stories, or timelines. This paper presents an
extensive investigation of various approaches for quantifying Fragmentation in
news recommendations. These approaches are evaluated both intrinsically, by
measuring performance on news story clustering, and extrinsically, by assessing
the Fragmentation scores of different simulated news recommender scenarios. Our
findings demonstrate that agglomerative hierarchical clustering coupled with
SentenceBERT text representation is substantially better at detecting
Fragmentation than earlier implementations. Additionally, the analysis of
simulated scenarios yields valuable insights and recommendations for
stakeholders concerning the measurement and interpretation of Fragmentation.

新闻推荐系统对塑造民主社会中的信息获取起着日益重要的作用，但是将推荐定制化于用户特定兴趣可能导致信息流的分散。信息获取的碎片化对公共领域的完整性造成挑战，进而影响民主与公共话语。本文通过自然语言处理（NLP）识别不同的新闻事件、故事或时间线，对量化新闻推荐中的碎片化提出了广泛的研究。我们评估了不同方法在新闻故事聚类上的性能，以及在不同模拟新闻推荐场景下的碎片化得分，发现基于凝聚层次聚类和 SentenceBERT 文本表示的方法明显优于以往的实现。此外，模拟场景的分析为相关方提供了关于测量和解释碎片化的有益见解和建议。