Political audience diversity and news reliability in algorithmic ranking

TL;DR

This paper proposes using the political diversity of a news website's audience as a signal to improve algorithmic ranking, thereby promoting more reliable news and reducing misinformation on social media platforms.

Contribution

It introduces a novel approach that incorporates audience political diversity into news ranking algorithms to enhance the promotion of trustworthy information.

Findings

Websites with less diverse audiences have lower journalistic standards.

Incorporating audience diversity improves recommendation trustworthiness.

The method particularly benefits users who frequently consume misinformation.

Abstract

Newsfeed algorithms frequently amplify misinformation and other low-quality content. How can social media platforms more effectively promote reliable information? Existing approaches are difficult to scale and vulnerable to manipulation. In this paper, we propose using the political diversity of a website's audience as a quality signal. Using news source reliability ratings from domain experts and web browsing data from a diverse sample of 6,890 U.S. citizens, we first show that websites with more extreme and less politically diverse audiences have lower journalistic standards. We then incorporate audience diversity into a standard collaborative filtering framework and show that our improved algorithm increases the trustworthiness of websites suggested to users -- especially those who most frequently consume misinformation -- while keeping recommendations relevant. These findings suggest that partisan audience diversity is a valuable signal of higher journalistic standards that should be incorporated into algorithmic ranking decisions.