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 paper, we mine a massive dataset 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."Comment: 25 pages, 10 figures, to appear in the Journal of the Association for
Information Science and Technology (JASIST