Beyond views: Measuring and predicting engagement in online videos

Publication Type:
Conference Proceeding
12th International AAAI Conference on Web and Social Media, ICWSM 2018, 2018, pp. 434 - 443
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Copyright © 2018, Association for the Advancement of Artificial Intelligence ( 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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