Biases in the Facebook News Feed: a Case Study on the Italian Elections

TL;DR

This study analyzes biases in Facebook's News Feed during the Italian elections, revealing significant visibility biases that favor certain publishers, especially at the top, even for politically neutral users.

Contribution

The paper introduces a reproducible measurement and modeling methodology to quantify and analyze visibility biases in Facebook's News Feed.

Findings

Bias is more prominent at the top of the News Feed.

Significant visibility bias exists even for neutral users.

The proposed model accurately captures publisher visibility dynamics.

Abstract

Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithms usually lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements and an analytical model to capture the visibility of publishers over a News Feed. First, measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility bias incurred by the users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition, we found that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views.