Constructing Political Coordinates: Aggregating Over the Opposition for Diverse News Recommendation
Eamon Earl, Chen Ding, Richard Valenzano, Drai Paulen-Patterson
TL;DR
The paper addresses the challenge that news recommender systems reinforce political biases and reduce exposure to diverse viewpoints. It introduces Constructed Political Coordinates (CPC), a bias-aware embedding that positions users relative to typology landmarks, and uses furthest-neighborhood aggregation within a CPC-based graph to encourage recommendations from oppositional peers. Empirical results show that CPC-based FNPC yields pointed diversity (higher Wasserstein distance and entropy) while maintaining engagement and handling heterogeneous typologies better than traditional rating-based baselines. The approach offers a robust, bias-aware extension to collaborative filtering that can mitigate filter bubbles and enhance civil discourse in democratic information ecosystems.
Abstract
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.