ArticleProceedings of the National Academy of Sciences of the United States of America2024
Causally estimating the effect of YouTube's recommender system using counterfactual bots.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Toward an integrated and globally equitable understanding of political polarization.Nature communications · 2026Review
- Intelligent Systems, Vulnerable Minds: A Framework for Radicalization to Violence in the Age of AI.Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc · 2026Review
- 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
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- The political effects of X's feed algorithm.Nature · 2026Article
- Control over Recommendation Algorithms in Heterogeneous Modular Systems with Dynamic Opinions.Entropy (Basel, Switzerland) · 2026Article
- The cultural ecology of social media.Evolutionary human sciences · 2026Article
- 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
- Article
- Misunderstanding the harms of online misinformation.Nature · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, critics of online platforms have raised concerns about the ability of recommendation algorithms to amplify problematic content, with potentially radicalizing consequences. However, attempts to evaluate the effect of recommenders have suffered from a lack of appropriate counterfactuals-what a user would have viewed in the absence of algorithmic recommendations-and hence cannot disentangle the effects of the algorithm from a user's intentions. Here we propose a method that we call "counterfactual bots" to causally estimate the role of algorithmic recommendations on the consumption of highly partisan content on YouTube. By comparing bots that replicate real users' consumption patterns with "counterfactual" bots that follow rule-based trajectories, we show that, on average, relying exclusively on the YouTube recommender results in less partisan consumption, where the effect is most pronounced for heavy partisan consumers. Following a similar method, we also show that if partisan consumers switch to moderate content, YouTube's sidebar recommender "forgets" their partisan preference within roughly 30 videos regardless of their prior history, while homepage recommendations shift more gradually toward moderate content. Overall, our findings indicate that, at least since the algorithm changes that YouTube implemented in 2019, individual consumption patterns mostly reflect individual preferences, where algorithmic recommendations play, if anything, a moderating role.
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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.