Fairness in Online Social Network Timelines: Measurements, Models and Mechanism Design

TL;DR

This paper introduces a reproducible methodology combining measurements, an analytical TTL-based model, and a fairness-oriented News Feed design to analyze and mitigate biases in Facebook's personalized content ranking.

Contribution

It presents a new TTL-based analytical model for News Feed visibility, validated by measurements, and proposes a fairness-driven redesign to reduce bias.

Findings

Significant bias exists in News Feed visibility, especially at top positions.

Bias persists even for politically neutral users.

A fairness-based design can improve transparency and reduce bias.

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 algorithm 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, an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. 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 and occupancy bias incurred by 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 find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.