Echo Chambers in the Age of Algorithms: An Audit of Twitter's Friend Recommender System

TL;DR

This study empirically audits Twitter's friend recommendation system, revealing it fosters echo chambers but also reduces exposure to misinformation compared to social endorsement-based networks.

Contribution

First in-situ empirical audit of Twitter's recommendation algorithm, analyzing its role in echo chamber formation and misinformation exposure during elections.

Findings

Recommender leads to dense, reciprocal echo chambers.

Recommender reduces political homogeneity compared to social endorsement.

Accounts following algorithm recommendations encounter less misinformation.

Abstract

The presence of political misinformation and ideological echo chambers on social media platforms is concerning given the important role that these sites play in the public's exposure to news and current events. Algorithmic systems employed on these platforms are presumed to play a role in these phenomena, but little is known about their mechanisms and effects. In this work, we conduct an algorithmic audit of Twitter's Who-To-Follow friend recommendation system, the first empirical audit that investigates the impact of this algorithm in-situ. We create automated Twitter accounts that initially follow left and right affiliated U.S. politicians during the 2022 U.S. midterm elections and then grow their information networks using the platform's recommender system. We pair the experiment with an observational study of Twitter users who already follow the same politicians. Broadly, we find that while following the recommendation algorithm leads accounts into dense and reciprocal neighborhoods that structurally resemble echo chambers, the recommender also results in less political homogeneity of a user's network compared to accounts growing their networks through social endorsement. Furthermore, accounts that exclusively followed users recommended by the algorithm had fewer opportunities to encounter content centered on false or misleading election narratives compared to choosing friends based on social endorsement.