Beyond views: Measuring and predicting engagement in online videos

Wu, S; Rizoiu, MA; Xie, L

Beyond views: Measuring and predicting engagement in online videos

Wu, S Rizoiu, MA

Xie, L

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Publication Type:: Conference Proceeding
Citation:: 12th International AAAI Conference on Web and Social Media, ICWSM 2018, 2018, pp. 434 - 443
Issue Date:: 2018-01-01

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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	2018-01-01	en_US
dc.identifier.citation	12th International AAAI Conference on Web and Social Media, ICWSM 2018, 2018, pp. 434 - 443	en_US
dc.identifier.isbn	9781577357988	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133235
dc.description.abstract	Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. The share of videos in the internet traffic has been growing, therefore understanding how videos capture attention on a global scale is also of growing importance. Most current research focus on modeling the number of views, but we argue that video engagement, or time spent watching is a more appropriate measure for resource allocation problems in attention, networking, and promotion activities. In this paper, we present a first large-scale measurement of video-level aggregate engagement from publicly available data streams, on a collection of 5.3 million YouTube videos published over two months in 2016. We study a set of metrics including time and the average percentage of a video watched. We define a new metric, relative engagement, that is calibrated against video properties and strongly correlate with recognized notions of quality. Moreover, we find that engagement measures of a video are stable over time, thus separating the concerns for modeling engagement and those for popularity - the latter is known to be unstable over time and driven by external promotions. We also find engagement metrics predictable from a cold-start setup, having most of its variance explained by video context, topics and channel information - R2=0.77. Our observations imply several prospective uses of engagement metrics - choosing engaging topics for video production, or promoting engaging videos in recommender systems.	en_US
dc.relation.ispartof	12th International AAAI Conference on Web and Social Media, ICWSM 2018	en_US
dc.title	Beyond views: Measuring and predicting engagement in online videos	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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	closed_access
pubs.publication-status	Published	en_US

Abstract:

Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. The share of videos in the internet traffic has been growing, therefore understanding how videos capture attention on a global scale is also of growing importance. Most current research focus on modeling the number of views, but we argue that video engagement, or time spent watching is a more appropriate measure for resource allocation problems in attention, networking, and promotion activities. In this paper, we present a first large-scale measurement of video-level aggregate engagement from publicly available data streams, on a collection of 5.3 million YouTube videos published over two months in 2016. We study a set of metrics including time and the average percentage of a video watched. We define a new metric, relative engagement, that is calibrated against video properties and strongly correlate with recognized notions of quality. Moreover, we find that engagement measures of a video are stable over time, thus separating the concerns for modeling engagement and those for popularity - the latter is known to be unstable over time and driven by external promotions. We also find engagement metrics predictable from a cold-start setup, having most of its variance explained by video context, topics and channel information - R2=0.77. Our observations imply several prospective uses of engagement metrics - choosing engaging topics for video production, or promoting engaging videos in recommender systems.

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http://hdl.handle.net/10453/133235