What’s “Up Next”? Investigating Algorithmic Recommendations on YouTube Across Issues and Over Time

Ariadna Matamoros-Fernández, Joanne E. Gray, Louisa Bartolo, Jean Burgess and Nicolas Suzor
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Ariadna Matamoros-Fernández: Digital Media Research Centre, Queensland University of Technology, Australia / School of Communication, Queensland University of Technology, Australia
Joanne E. Gray: Digital Media Research Centre, Queensland University of Technology, Australia / School of Communication, Queensland University of Technology, Australia
Louisa Bartolo: Digital Media Research Centre, Queensland University of Technology, Australia / School of Communication, Queensland University of Technology, Australia
Jean Burgess: Digital Media Research Centre, Queensland University of Technology, Australia / School of Communication, Queensland University of Technology, Australia
Nicolas Suzor: Digital Media Research Centre, Queensland University of Technology, Australia / School of Law, Queensland University of Technology, Australia

Media and Communication, 2021, vol. 9, issue 4, 234-249

Abstract: YouTube’s “up next” feature algorithmically selects, suggests, and displays videos to watch after the one that is currently playing. This feature has been criticized for limiting users’ exposure to a range of diverse media content and information sources; meanwhile, YouTube has reported that they have implemented various technical and policy changes to address these concerns. However, there is little publicly available data to support either the existing concerns or YouTube’s claims of having addressed them. Drawing on the idea of “platform observability,” this article combines computational and qualitative methods to investigate the types of content that the algorithms underpinning YouTube’s “up next” feature amplify over time, using three keyword search terms associated with sociocultural issues where concerns have been raised about YouTube’s role: “coronavirus,” “feminism,” and “beauty.” Over six weeks, we collected the videos (and their metadata, including channel IDs) that were highly ranked in the search results for each keyword, as well as the highly ranked recommendations associated with the videos. We repeated this exercise for three steps in the recommendation chain and then examined patterns in the recommended videos (and the channels that uploaded the videos) for each query and their variation over time. We found evidence of YouTube’s stated efforts to boost “authoritative” media outlets, but at the same time, misleading and controversial content continues to be recommended. We also found that while algorithmic recommendations offer diversity in videos over time, there are clear “winners” at the channel level that are given a visibility boost in YouTube’s “up next” feature. However, these impacts are attenuated differently depending on the nature of the issue.

Keywords: algorithms; automation; content moderation; digital methods; platform governance; YouTube (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:cog:meanco:v9:y:2021:i:4:p:234-249

DOI: 10.17645/mac.v9i4.4184

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