Quantifying Biases in Online Information Exposure
By Dimitar Nikolov, Mounia Lalmas, Alessandro Flammini, Filippo Menczer. 2018
97 citations recorded by the source corpus.
Our consumption of online information is mediated by filtering, ranking, and recommendation algorithms that introduce unintentional biases as they attempt to deliver relevant and engaging content. It has been suggested that our reliance on online technologies such as search engines and social media may limit exposure to diverse points of view and make us vulnerable to manipulation by disinformation. In this article, we mine a massive data set of web traffic to quantify two kinds of bias: (i) homogeneity bias, which is the tendency to consume content from a narrow set of information sources, and (ii) popularity bias, which is the selective exposure to content from top sites. Our analysis reveals different bias levels across several widely used web platforms. Search exposes users to a diverse set of sources, while social media traffic tends to exhibit high popularity and homogeneity bias. When we focus our analysis on traffic to news sites, we find higher levels of popularity bias, with smaller differences across applications. Overall, our results quantify the extent to which our choices of online systems confine us inside “social bubbles.”
Subfields: Communication, Sociology and Political Science
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Citations in the corpus
- A Transformer-based Framework for Neutralizing and Reversing the Political Polarity of News Articles (2021, background): “Such phenomenon aggravates interactive polarized clusters known as “echo chambers", which reinforces political disposition of both the reader and the media [74].”