Systematic partisan content skews in TikTok during the 2024 US elections - Hazem Ibrahim; HyunSeok Daniel Jang; Nouar Aldahoul; Aaron R. Kaufman; Talal Rahwan; Yasir Zaki (Nature, 2026)
Abstract Social media platforms increasingly mediate political information exposure, yet the role of algorithmic curation in shaping political exposure remains contested 1,2 . This question is difficult to resolve on platforms in which users retain substantial control over their feeds 3,4 . The ‘For You’ feed of TikTok, which delivers content almost entirely through algorithmic recommendation, offers a setting in which user agency is sharply constrained. Here we show, through 323 audit experiments with controlled ‘sock puppet’ accounts seeded with Democratic or Republican content across three US states, that accounts seeded with partisan content exhibited systematic, asymmetric differences in partisan exposure. Across more than 280,000 recommendations collected over 27 weeks during the 2024 US presidential election campaign, Republican-seeded accounts received about 11.5% more co-partisan content than Democratic-seeded accounts, whereas Democratic-seeded accounts were exposed to about 7.5% more cross-partisan content—largely anti-Democratic material—even after adjusting for engagement metrics. These asymmetries are concentrated among high-reach Republican channels and in specific policy domains, including immigration, crime and foreign policy for Democrats, and abortion for Republicans. Our findings show partisan imbalances in political information exposure on a platform dominated by algorithmic recommendations, with implications for platform governance and democratic discourse.