# Improving and Evaluating the Detection of Fragmentation in News Recommendations with the Clustering of News Story Chains

arXiv: https://arxiv.org/abs/2309.06192 · HTML: https://mlsanity.com/paper/2309.06192/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Recommenders with a mission: assessing diversity in newsrecommendations](https://mlsanity.com/paper/2012.10185/) (2020)
- [News Headline Grouping as a Challenging NLU Task](https://mlsanity.com/paper/2105.05391/) (2021)
- [Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection](https://mlsanity.com/paper/2203.05824/) (2022)
- [RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations](https://mlsanity.com/paper/2209.13520/) (2022)
- [Operationalizing Framing to Support Multiperspective Recommendations of Opinion Pieces](https://mlsanity.com/paper/2101.06141/) (2021)
- [Beyond Digital "Echo Chambers": The Role of Viewpoint Diversity in Political Discussion](https://mlsanity.com/paper/2212.09056/) (2022)
- [Understanding news story chains using information retrieval and network clustering techniques](https://mlsanity.com/paper/1801.07988/) (2018)

## Abstract (arXiv)

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.