102 citations · 323 across the 41 of their papers we have counts for
51 papers
Causally estimating the effect of YouTube's recommender system using counterfactual bots
Homa Hosseinmardi, Amir Ghasemian, Miguel Rivera-Lanas +3
In recent years, critics of online platforms have raised concerns about the ability of recommendation algorithms to amplify problematic content, with potentially radicalizing conse…
Tube2Vec: Social and Semantic Embeddings of YouTube Channels
Léopaul Boesinger, Manoel Horta Ribeiro, Veniamin Veselovsky +1
Research using YouTube data often explores social and semantic dimensions of channels and videos. Typically, analyses rely on laborious manual annotation of content and content cre…
Orphan Articles: The Dark Matter of Wikipedia
Akhil Arora, Robert West, Martin Gerlach
With 60M articles in more than 300 language versions, Wikipedia is the largest platform for open and freely accessible knowledge. While the available content has been growing conti…
Open Access Improves the Dissemination of Science: Insights from Wikipedia
Puyu Yang, Ahad Shoaib, Robert West +1
Wikipedia is a well-known platform for disseminating knowledge, and scientific sources, such as journal articles, play a critical role in supporting its mission. The open access mo…
Curious Rhythms: Temporal Regularities of Wikipedia Consumption
Tiziano Piccardi, Martin Gerlach, Robert West
Wikipedia, in its role as the world's largest encyclopedia, serves a broad range of information needs. Although previous studies have noted that Wikipedia users' information needs…
The Amplification Paradox in Recommender Systems
Manoel Horta Ribeiro, Veniamin Veselovsky, Robert West
Automated audits of recommender systems found that blindly following recommendations leads users to increasingly partisan, conspiratorial, or false content. At the same time, studi…