Causally estimating the effect of YouTube's recommender system using counterfactual bots
成果类型：
Article
署名作者：
Hosseinmardi, Homa; Ghasemian, Amir; Rivera-Lanas, Miguel; Ribeiro, Manoel Horta; West, Robert; Bail, Christopher A.
署名单位：
University of Pennsylvania; University of Pennsylvania; Yale University; Carnegie Mellon University; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Pennsylvania
刊物名称：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
ISSN/ISSBN：
0027-11653
DOI：
10.1073/pnas.2313377121
发表日期：
2024-02-20
关键词：
摘要：
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.