Political audience diversity and news reliability in algorithmic ranking
- URL: http://arxiv.org/abs/2007.08078v2
- Date: Sat, 6 Mar 2021 15:11:31 GMT
- Title: Political audience diversity and news reliability in algorithmic ranking
- Authors: Saumya Bhadani, Shun Yamaya, Alessandro Flammini, Filippo Menczer,
Giovanni Luca Ciampaglia, and Brendan Nyhan
- Abstract summary: 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.
- Score: 54.23273310155137
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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