Political audience diversity and news reliability in algorithmic ranking

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 w...

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Bibliographic Details
Published in:arXiv.org 2021-03
Main Authors: Bhadani, Saumya, Yamaya, Shun, Flammini, Alessandro, Menczer, Filippo, Ciampaglia, Giovanni Luca, Nyhan, Brendan
Format: Article
Language:English
Subjects:
Algorithms
Audiences
Browsing
Digital media
False information
News
Ranking
Reliability
Signal quality
Websites
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2007.08078