Abstract

Approximately 50% of tweets in ’s user timelines are personalized recommendations from accounts they do not follow. This raises a critical question: What political content are users exposed to beyond their established networks, and what implications does this have for democratic discourse online? In this paper, we present a six-week audit of ’s algorithmic content recommendations during the 2024 U.S. Presidential Election by deploying 120 sock-puppet monitoring accounts to capture tweets from their personalized “For You” timelines. Our objective is to quantify out-of-network content exposure for right- and left-leaning account profiles and assess any potential inequalities and biases in political exposure. Our findings indicate that ’s algorithm skews exposure toward a few high-popularity users across all monitoring accounts, with right-leaning accounts experiencing the highest level of exposure …

Date

2025

Authors

Jinyi Ye, Luca Luceri, Emilio Ferrara

Book

Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency

Pages

2349-2362