Quantifying Biases in Online Information Exposure

TL;DR

This paper analyzes how online algorithms create biases like homogeneity and popularity bias, limiting diverse information exposure and fostering social bubbles, based on large-scale Web traffic data.

Contribution

It provides a quantitative analysis of biases in online information exposure across various platforms, revealing differences in bias levels and their implications.

Findings

Search engines promote diverse sources

Social media shows high popularity bias

News traffic exhibits strong popularity bias

Abstract

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."