Echo Chambers in the Age of Algorithms: An Audit of Twitter’s Friend Recommender System
Open accessAbstract
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.
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Algorithmic recommendations can reinforce echo chambers not just in the composition of social networks but also in the ideological framing of content circulating in the network.
“The X algorithm's amplification of ideologically aligned out-of-network accounts, along with the reduced exposure to opposing viewpoints, suggests that algorithmic recommendations can reinforce echo chambers not just in the composition of social networks [12] but also in the ideological framing of content circulating in the network.”
From Auditing Political Exposure Bias: Algorithmic Amplification on Twitter/X During the 2024 U.S. Presidential Election (2025) · cites this paper as [12]
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- Authors
- Duskin, K., Schafer, J. S., West, J. D., & Spiro, E. S.
- Affiliations
- University of Washington (US)
- Venue
- ACM Web Science Conference
- Publisher
- ACM
- Published
- 2024-05-21
- Pages
- 11-21
- OpenAlex
- W4394902606
- Cited by
- 17
- References
- 21
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 1
- FWCI
- 22.30
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Web Science Conference (WebSci) (publishedVersion, cc-by-nc)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- William and Flora Hewlett Foundation (2022-00952-GRA)
- John S. and James L. Knight Foundation (G-2019-58788)
- Election Trust Initiative
- NSF (National Science Foundation) (2120496, DGE-2140004, 1749815)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Keywords
- echo chambers, algorithmic auditing, Twitter, friend recommendation, political misinformation, political polarization, social endorsement, automated accounts, election misinformation, network homophily, social media algorithms
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