# Recommenders with a mission: assessing diversity in newsrecommendations

arXiv: https://arxiv.org/abs/2012.10185 · HTML: https://mlsanity.com/paper/2012.10185/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2020.

## Built on by (most cited first, 15 of 15)

- [Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review](https://mlsanity.com/paper/2307.01221/) (2023)
- [EB-NeRD: A Large-Scale Dataset for News Recommendation](https://mlsanity.com/paper/2410.03432/) (2024)
- [RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations](https://mlsanity.com/paper/2209.13520/) (2022)
- [Beyond Digital "Echo Chambers": The Role of Viewpoint Diversity in Political Discussion](https://mlsanity.com/paper/2212.09056/) (2022)
- [PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder](https://mlsanity.com/paper/2505.24646/) (2025)
- [Structuring the Space of Perspectives](https://mlsanity.com/paper/2608.12113/) (2026)
- [Improving and Evaluating the Detection of Fragmentation in News Recommendations with the Clustering of News Story Chains](https://mlsanity.com/paper/2309.06192/) (2023)
- [Online and Offline Evaluations of Collaborative Filtering and Content Based Recommender Systems](https://mlsanity.com/paper/2411.01354/) (2024)
- [Towards Multi-Aspect Diversification of News Recommendations Using Neuro-Symbolic AI for Individual and Societal Benefit](https://mlsanity.com/paper/2509.02220/) (2025)
- [Controlled Personalization in Legacy Media Online Services: A Case Study in News Recommendation](https://mlsanity.com/paper/2510.09136/) (2025)
- [Constructing Political Coordinates: Aggregating Over the Opposition for Diverse News Recommendation](https://mlsanity.com/paper/2511.17574/) (2025)
- [Integrating Large Language Models and Knowledge Graphs to Capture Political Viewpoints in News Media](https://mlsanity.com/paper/2512.14887/) (2025)
- [Information Access of the Oppressed: Freirean Design for Emancipatory Information Access](https://mlsanity.com/paper/2601.09600/) (2026)
- [Normative Alignment of Recommender Systems via Internal Label Shift](https://mlsanity.com/paper/2607.10915/) (2026)
- [WildSEEK: Evaluating Language Models for Information-Seeking](https://mlsanity.com/paper/2608.30683/) (2026)

## Abstract (arXiv)

News recommenders help users to find relevant online content and have the potential to fulfill a crucial role in a democratic society, directing the scarce attention of citizens towards the information that is most important to them. Simultaneously, recent concerns about so-called filter bubbles, misinformation and selective exposure are symptomatic of the disruptive potential of these digital news recommenders. Recommender systems can make or break filter bubbles, and as such can be instrumental in creating either a more closed or a more open internet. Current approaches to evaluating recommender systems are often focused on measuring an increase in user clicks and short-term engagement, rather than measuring the user's longer term interest in diverse and important information. This paper aims to bridge the gap between normative notions of diversity, rooted in democratic theory, and quantitative metrics necessary for evaluating the recommender system. We propose a set of metrics grounded in social science interpretations of diversity and suggest ways for practical implementations.