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

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 ha...

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Autores principales:	Ariadna Matamoros-Fernández, Joanne E. Gray, Louisa Bartolo, Jean Burgess, Nicolas Suzor
Formato:	article
Lenguaje:	EN
Publicado:	Cogitatio 2021
Materias:	algorithms automation content moderation digital methods platform governance youtube Communication. Mass media P87-96
Acceso en línea:	https://doaj.org/article/5dc3c6788b2642c5bf3fdf3e7687b500
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spelling	oai:doaj.org-article:5dc3c6788b2642c5bf3fdf3e7687b5002021-11-18T11:14:13ZWhat’s “Up Next”? Investigating Algorithmic Recommendations on YouTube Across Issues and Over Time2183-243910.17645/mac.v9i4.4184https://doaj.org/article/5dc3c6788b2642c5bf3fdf3e7687b5002021-11-01T00:00:00Zhttps://www.cogitatiopress.com/mediaandcommunication/article/view/4184https://doaj.org/toc/2183-2439YouTube’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.Ariadna Matamoros-FernándezJoanne E. GrayLouisa BartoloJean BurgessNicolas SuzorCogitatioarticlealgorithmsautomationcontent moderationdigital methodsplatform governanceyoutubeCommunication. Mass mediaP87-96ENMedia and Communication, Vol 9, Iss 4, Pp 234-249 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	algorithms automation content moderation digital methods platform governance youtube Communication. Mass media P87-96
spellingShingle	algorithms automation content moderation digital methods platform governance youtube Communication. Mass media P87-96 Ariadna Matamoros-Fernández Joanne E. Gray Louisa Bartolo Jean Burgess Nicolas Suzor What’s “Up Next”? Investigating Algorithmic Recommendations on YouTube Across Issues and Over Time
description	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.
format	article
author	Ariadna Matamoros-Fernández Joanne E. Gray Louisa Bartolo Jean Burgess Nicolas Suzor
author_facet	Ariadna Matamoros-Fernández Joanne E. Gray Louisa Bartolo Jean Burgess Nicolas Suzor
author_sort	Ariadna Matamoros-Fernández
title	What’s “Up Next”? Investigating Algorithmic Recommendations on YouTube Across Issues and Over Time
title_short	What’s “Up Next”? Investigating Algorithmic Recommendations on YouTube Across Issues and Over Time
title_full	What’s “Up Next”? Investigating Algorithmic Recommendations on YouTube Across Issues and Over Time
title_fullStr	What’s “Up Next”? Investigating Algorithmic Recommendations on YouTube Across Issues and Over Time
title_full_unstemmed	What’s “Up Next”? Investigating Algorithmic Recommendations on YouTube Across Issues and Over Time
title_sort	what’s “up next”? investigating algorithmic recommendations on youtube across issues and over time
publisher	Cogitatio
publishDate	2021
url	https://doaj.org/article/5dc3c6788b2642c5bf3fdf3e7687b500
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What’s “Up Next”? Investigating Algorithmic Recommendations on YouTube Across Issues and Over Time

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