Political audience diversity and news reliability in algorithmic ranking

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.

47 pages, 23 figures, 5 tables (including supplementary materials). Nat Hum Behav (2022)

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Cited by in corpus (3)

• Search engine effects on news consumption: ranking and representativeness outweigh familiarity in news selection
• The Impact of Disinformation on a Controversial Debate on Social Media
• Differential impact from individual versus collective misinformation tagging on the diversity of Twitter (X) information engagement and mobility