ArticleProceedings of the National Academy of Sciences of the United States of America2023
Auditing YouTube's recommendation system for ideologically congenial, extreme, and problematic recommendations.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Toward an integrated and globally equitable understanding of political polarization.Nature communications · 2026Review
- Quality and reliability of short videos about long COVID on bilibili and tiktok: cross-sectional study under health community construction background in China.BMC public health · 2026Article
- Public health implications of allergic rhinitis information on YouTube and Bilibili: a cross-cultural analysis of content quality, engagement, and seasonal trends.BMC public health · 2026Article
- Article
- Quality and reliability of hyperlipidemia-related short videos on mainstream social media platforms: a cross-sectional study.Scientific reports · 2026Article
- Ad-based social media interventions increase belief accuracy and generate pro-social opinions among non-news readers.PloS one · 2026Article
- The cultural ecology of social media.Evolutionary human sciences · 2026Article
- The rising safety concerns of deep recommender systems.Innovation (Cambridge (Mass.)) · 2025Review
- Independently testing prosocial interventions: Methods and recommendations from 31 researchers.Annals of the New York Academy of Sciences · 2025Review
- Quality and content evaluation of thyroid eye disease treatment information on TikTok and Bilibili.Scientific reports · 2025Article
- Engagement, user satisfaction, and the amplification of divisive content on social media.PNAS nexus · 2025Article
- Short-term exposure to filter-bubble recommendation systems has limited polarization effects: Naturalistic experiments on YouTube.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Citation manipulation through citation mills and pre-print servers.Scientific reports · 2025Article
- A multilevel network approach to revealing patterns of online political selective exposure.PloS one · 2025Article
- Causally estimating the effect of YouTube's recommender system using counterfactual bots.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Auditing YouTube's recommendation system for ideologically congenial, extreme, and problematic recommendations.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Algorithms of social media platforms are often criticized for recommending ideologically congenial and radical content to their users. Despite these concerns, evidence on such filter bubbles and rabbit holes of radicalization is inconclusive. We conduct an audit of the platform using 100,000 sock puppets that allow us to systematically and at scale isolate the influence of the algorithm in recommendations. We test 1) whether recommended videos are congenial with regard to users' ideology, especially deeper in the watch trail and whether 2) recommendations deeper in the trail become progressively more extreme and come from problematic channels. We find that YouTube's algorithm recommends congenial content to its partisan users, although some moderate and cross-cutting exposure is possible and that congenial recommendations increase deeper in the trail for right-leaning users. We do not find meaningful increases in ideological extremity of recommendations deeper in the trail, yet we show that a growing proportion of recommendations comes from channels categorized as problematic (e.g., "IDW," "Alt-right," "Conspiracy," and "QAnon"), with this increase being most pronounced among the very-right users. Although the proportion of these problematic recommendations is low (max of 2.5%), they are still encountered by over 36.1% of users and up to 40% in the case of very-right users.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.