Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | NORMalize 2023: Normative Design and Evaluation of Recommender Systems 2023 |
| Subtitle of host publication | Proceedings of the First Workshop on the Normative Design and Evaluation of Recommender Systems (NORMalize 2023) co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023) Singapore, Singapore, September 19, 2023 |
| Editors | Sanne Vrijenhoek, Lien Michiels, Johannes Kruse, Alain Starke, Jordi Viader Guerrero, Nina Tintarev |
| Publisher | CEUR Proceedings |
| Pages | 1-16 |
| Number of pages | 16 |
| Publication status | Published - 2023 |
| Event | 1st Workshop on the Normative Design and Evaluation of Recommender Systems, NORMalize 2023 - Singapore, Singapore Duration: 19 Sept 2023 → … |
Publication series
| Name | CEUR Workshop Proceedings |
|---|---|
| Publisher | CEUR Workshop Proceedings |
| Volume | 3639 |
| ISSN (Print) | 1613-0073 |
Conference
| Conference | 1st Workshop on the Normative Design and Evaluation of Recommender Systems, NORMalize 2023 |
|---|---|
| Country/Territory | Singapore |
| City | Singapore |
| Period | 19/09/23 → … |
Bibliographical note
Publisher Copyright:© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)
Keywords
- natural language processing
- news recommendation
- news story clustering
- operationalization