Cross-Cutting Political Awareness through Diverse News Recommendations

TL;DR

This paper presents a novel recommendation system that identifies user political leanings and suggests diverse news to broaden perspectives and reduce polarization.

Contribution

It introduces a computational framework that personalizes news recommendations based on political leanings to enhance view diversity and mitigate polarization.

Findings

Increases exposure to diverse political viewpoints.

Reduces reinforcement of existing biases.

Promotes acceptance of different opinions.

Abstract

The suggestions generated by most existing recommender systems are known to suffer from a lack of diversity, and other issues like popularity bias. As a result, they have been observed to promote well-known "blockbuster" items, and to present users with "more of the same" choices that entrench their existing beliefs and biases. This limits users' exposure to diverse viewpoints and potentially increases political polarization. To promote the diversity of views, we developed a novel computational framework that can identify the political leanings of users and the news items they share on online social networks. Based on such information, our system can recommend news items that purposefully expose users to different viewpoints and increase the diversity of their information "diet." Our research on recommendation diversity and political polarization helps us to develop algorithms that measure each user's reaction %to diverse viewpoints and adjust the recommendation accordingly. The result is an approach that exposes users to a variety of political views and will, hopefully, broaden their acceptance (not necessarily the agreement) of various opinions.