Quantifying Biases in Online Information Exposure

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 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.”

Social influence and unfollowing accelerate the emergence of echo chambers

“…The terms filter bubble [37] and echo chamber [38] have been coined to refer to two different algorithmic pathways to opinion fragmentation, both related to the way algorithms filter and rank information. The first refers to search engines [39], the second to social media feeds [40,41].…”

Right and left, partisanship predicts (asymmetric) vulnerability to misinformation

“…This conclusion was based on the assumption that all posts by friends are equally likely to be seen. However, since social media platforms rank content based on popularity and personalization (Nikolov et al, 2019), highly-engaging false news would get higher exposure. In fact, algorithmic bias may amplify exposure to low-quality content (Ciampaglia et al, 2018).…”

Dataset bias: A case study for visual question answering

“…Ethical issues in machine learning are being studied by researchers in both academia (X. Jiang, Sun, Yang, Zhuge, & Yao, ; Katell, ; Lepri, Oliver, Letouzé, Pentland, & Vinck, ; Nikolov, Lalmas, Flammini, & Menczer, ; Wilkie & Azzopardi, ) and industry , (Bellamy et al, ). The ubiquity of machine learning applications is a reason for the increasing concern.…”