Estimating attention flow in online video networks

Wu, S; Rizoiu, MA; Xie, L

Estimating attention flow in online video networks

Wu, S Rizoiu, MA

Xie, L

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Publication Type:: Journal Article
Citation:: Proceedings of the ACM on Human-Computer Interaction, 2019, 3 (CSCW)
Issue Date:: 2019-11-01

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Field	Value	Language
dc.contributor.author	Wu, S	en_US
dc.contributor.author	Rizoiu, MA https://orcid.org/0000-0003-0381-669X	en_US
dc.contributor.author	Xie, L	en_US
dc.date.issued	2019-11-01	en_US
dc.identifier.citation	Proceedings of the ACM on Human-Computer Interaction, 2019, 3 (CSCW)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137869
dc.description.abstract	© 2019 Association for Computing Machinery. Online videos have shown tremendous increase in Internet traffic. Most video hosting sites implement recommender systems, which connect the videos into a directed network and conceptually act as a source of pathways for users to navigate. At present, little is known about how human attention is allocated over such large-scale networks, and about the impacts of the recommender systems. In this paper, we first construct the Vevo network — a YouTube video network with 60,740 music videos interconnected by the recommendation links, and we collect their associated viewing dynamics. This results in a total of 310 million views every day over a period of 9 weeks. Next, we present large-scale measurements that connect the structure of the recommendation network and the video attention dynamics. We use the bow-tie structure to characterize the Vevo network and we find that its core component (23.1% of the videos), which occupies most of the attention (82.6% of the views), is made out of videos that are mainly recommended among themselves. This is indicative of the links between video recommendation and the inequality of attention allocation. Finally, we address the task of estimating the attention flow in the video recommendation network. We propose a model that accounts for the network effects for predicting video popularity, and we show it consistently outperforms the baselines. This model also identifies a group of artists gaining attention because of the recommendation network. Altogether, our observations and our models provide a new set of tools to better understand the impacts of recommender systems on collective social attention.	en_US
dc.relation.ispartof	Proceedings of the ACM on Human-Computer Interaction	en_US
dc.relation.isbasedon	10.1145/3359285	en_US
dc.title	Estimating attention flow in online video networks	en_US
dc.type	Journal Article
utslib.citation.volume	CSCW	en_US
utslib.citation.volume	3	en_US
utslib.for	080602 Computer-Human Interaction	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access
pubs.issue	CSCW	en_US
pubs.publication-status	Published	en_US
pubs.volume	3	en_US

Abstract:

© 2019 Association for Computing Machinery. Online videos have shown tremendous increase in Internet traffic. Most video hosting sites implement recommender systems, which connect the videos into a directed network and conceptually act as a source of pathways for users to navigate. At present, little is known about how human attention is allocated over such large-scale networks, and about the impacts of the recommender systems. In this paper, we first construct the Vevo network — a YouTube video network with 60,740 music videos interconnected by the recommendation links, and we collect their associated viewing dynamics. This results in a total of 310 million views every day over a period of 9 weeks. Next, we present large-scale measurements that connect the structure of the recommendation network and the video attention dynamics. We use the bow-tie structure to characterize the Vevo network and we find that its core component (23.1% of the videos), which occupies most of the attention (82.6% of the views), is made out of videos that are mainly recommended among themselves. This is indicative of the links between video recommendation and the inequality of attention allocation. Finally, we address the task of estimating the attention flow in the video recommendation network. We propose a model that accounts for the network effects for predicting video popularity, and we show it consistently outperforms the baselines. This model also identifies a group of artists gaining attention because of the recommendation network. Altogether, our observations and our models provide a new set of tools to better understand the impacts of recommender systems on collective social attention.

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http://hdl.handle.net/10453/137869