# Constructing Political Coordinates: Aggregating Over the Opposition for Diverse News Recommendation

arXiv: https://arxiv.org/abs/2511.17574 · HTML: https://mlsanity.com/paper/2511.17574/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## 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)
- [How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility](https://mlsanity.com/paper/1710.11214/) (2017)
- [Estimating and Penalizing Induced Preference Shifts in Recommender Systems](https://mlsanity.com/paper/2204.11966/) (2022)
- [Recommenders with a mission: assessing diversity in newsrecommendations](https://mlsanity.com/paper/2012.10185/) (2020)
- [Emergence of polarized ideological opinions in multidimensional topic spaces](https://mlsanity.com/paper/2007.00601/) (2020)
- [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)
- [Beyond Digital "Echo Chambers": The Role of Viewpoint Diversity in Political Discussion](https://mlsanity.com/paper/2212.09056/) (2022)
- [Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns](https://mlsanity.com/paper/2402.15013/) (2024)
- [A Multi-Factorial Analysis of Polarization on Social Media](https://mlsanity.com/paper/2306.00032/) (2023)
- [Deliberate Exposure to Opposing Views and its Association with Behavior and Rewards on Political Communities](https://mlsanity.com/paper/2401.14608/) (2024)

## Abstract (arXiv)

In the past two decades, open access to news and information has increased rapidly, empowering educated political growth within democratic societies. News recommender systems (NRSs) have shown to be useful in this process, minimizing political disengagement and information overload by providing individuals with articles on topics that matter to them. Unfortunately, NRSs often conflate underlying user interest with the partisan bias of the articles in their reading history and with the most popular biases present in the coverage of their favored topics. Over extended interaction, this can result in the formation of filter bubbles and the polarization of user partisanship. In this paper, we propose a novel embedding space called Constructed Political Coordinates (CPC), which models the political partisanship of users over a given topic-space, relative to a larger sample population. We apply a simple collaborative filtering (CF) framework using CPC-based correlation to recommend articles sourced from oppositional users, who have different biases from the user in question. We compare against classical CF methods and find that CPC-based methods promote pointed bias diversity and better match the true political tolerance of users, while classical methods implicitly exploit biases to maximize interaction.