Algorithmic Amplification of Politics on Twitter
Open accessAbstract
Significance: The role of social media in political discourse has been the topic of intense scholarly and public debate. Politicians and commentators from all sides allege that Twitter’s algorithms amplify their opponents’ voices, or silence theirs. Policy makers and researchers have thus called for increased transparency on how algorithms influence exposure to political content on the platform. Based on a massive-scale experiment involving millions of Twitter users, a fine-grained analysis of political parties in seven countries, and 6.2 million news articles shared in the United States, this study carries out the most comprehensive audit of an algorithmic recommender system and its effects on political content. Results unveil that the political right enjoys higher amplification compared to the political left.
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The simple chronological ranking is easy to implement, was originally used on both Facebook and Instagram, continues to be an option on multiple social media platforms, and matches an alternative proposed by policy-makers and civil society.
“The simple chronological ranking, in which the most recent item is pre-sented at the top of one’s feed, is both easy to implement—it was originally used on both Facebook and Instagram and continues to be an option on multiple social media platforms (12)—and matches an alternative currently being proposed in public debates by policy-makers and members of civil society (13).”
From How do social media feed algorithms affect attitudes and behavior in an election campaign? (2023) · cites this paper as [12]
The Abstract (page unavailable) says Twitter originally used a reverse-chronological feed, and Experimental Setup (page unavailable) says users in the personalized-timeline group could opt out. Those passages support Twitter as one example, but the paper does not establish that chronological ranking is easy to implement, was originally used on Facebook and Instagram, or remains an option on multiple platforms. The public-debate clause carries a separate citation and is not assessed here. (LLM, conf=0.86)
Experimental — accuracy not yet measuredSome studies have found that Twitter's algorithms tend to amplify content from right-leaning media sources and politicians more than their left-leaning counterparts.
“Some have found that Twitter's algorithms tend to amplify content from right-leaning media sources and politicians more than their left-leaning counterparts [15,20].”
From Auditing Political Exposure Bias: Algorithmic Amplification on Twitter/X During the 2024 U.S. Presidential Election (2025) · cites this paper as [20]
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- Authors
- Huszár, F., Ktena, S. I., O’Brien, C., Belli, L., Schlaikjer, A., & Hardt, M.
- Affiliations
- Twitter (United States) (US)
- University of California, Berkeley (US)
- Corresponding author
- Huszár, Ferenc
- Belli, Luca
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2021-12-21
- Volume
- 119
- Issue
- 1
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC8740571
- OpenAlex
- W3206593531
- Cited by
- 65
- References
- 10
- Citations by year
- 2026: 2 · 2025: 11 · 2024: 12 · 2023: 27 · 2022: 11 · 2021: 2
- FWCI
- 17.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Engineering and Physical Sciences Research Council (EP/W002965/1)
- Topics
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Keywords
- algorithmic amplification, Twitter, personalization algorithms, content ranking, political polarization, left-right asymmetry, randomized experiments
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