TikTok's recommendations skewed towards Republican content during the 2024 U.S. presidential race

• URL: http://arxiv.org/abs/2501.17831v2
• Date: Wed, 07 May 2025 07:33:29 GMT
• Title: TikTok's recommendations skewed towards Republican content during the 2024 U.S. presidential race
• Authors: Hazem Ibrahim, HyunSeok Daniel Jang, Nouar Aldahoul, Aaron R. Kaufman, Talal Rahwan, Yasir Zaki,
• Abstract summary: TikTok is a major force among social media platforms with over a billion monthly active users worldwide and 170 million in the U.S.<n>Despite concerns, there is scant research investigating TikTok's recommendation algorithm for political biases.<n>We fill this gap by conducting 323 independent algorithmic audit experiments testing partisan content recommendations in the lead-up to the 2024 U.S. presidential elections.
• Score: 1.340487372205839
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: TikTok is a major force among social media platforms with over a billion monthly active users worldwide and 170 million in the United States. The platform's status as a key news source, particularly among younger demographics, raises concerns about its potential influence on politics in the U.S. and globally. Despite these concerns, there is scant research investigating TikTok's recommendation algorithm for political biases. We fill this gap by conducting 323 independent algorithmic audit experiments testing partisan content recommendations in the lead-up to the 2024 U.S. presidential elections. Specifically, we create hundreds of "sock puppet" TikTok accounts in Texas, New York, and Georgia, seeding them with varying partisan content and collecting algorithmic content recommendations for each of them. Collectively, these accounts viewed ~394,000 videos from April 30th to November 11th, 2024, which we label for political and partisan content. Our analysis reveals significant asymmetries in content distribution: Republican-seeded accounts received ~11.8% more party-aligned recommendations compared to their Democratic-seeded counterparts, and Democratic-seeded accounts were exposed to ~7.5% more opposite-party recommendations on average. These asymmetries exist across all three states and persist when accounting for video- and channel-level engagement metrics such as likes, views, shares, comments, and followers, and are driven primarily by negative partisanship content. Our findings provide insights into the inner workings of TikTok's recommendation algorithm during a critical election period, raising fundamental questions about platform neutrality.

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