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Newsfeed algorithms frequently amplify misinformation and other low-qualitycontent. How can social media platforms more effectively promote reliableinformation? Existing approaches are difficult to scale and vulnerable tomanipulation. In this paper, we propose using the political diversity of awebsite's audience as a quality signal. Using news source reliability ratingsfrom 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 politicallydiverse audiences have lower journalistic standards. We then incorporateaudience diversity into a standard collaborative filtering framework and showthat our improved algorithm increases the trustworthiness of websites suggestedto users -- especially those who most frequently consume misinformation --while keeping recommendations relevant. These findings suggest that partisanaudience diversity is a valuable signal of higher journalistic standards thatshould be incorporated into algorithmic ranking decisions.

Political audience diversity and news reliability in algorithmic ranking