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

TL;DR

This paper introduces Constructed Political Coordinates (CPC), an embedding space that models user political partisanship to improve news recommendations by promoting bias diversity and reducing polarization.

Contribution

The paper presents a novel CPC embedding space and a collaborative filtering approach that recommends articles from oppositional users to enhance political diversity in news recommendations.

Findings

CPC-based methods promote bias diversity in recommendations.

CPC methods better match users' true political tolerance.

Classical CF methods tend to exploit biases to maximize interaction.

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.