Beyond Views: Measuring and Predicting Engagement on YouTube Videos
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This work studies engagement, or time spent watching online videos. Most current work focuses on modeling the number of views, which is known to be inadequate More broadly, engagement has been studied in reading behavior of news and web pages, click-through in online ads, but for videos, a robust set of engagement metrics do not exist yet. We study a set of aggregate engagement metrics including watch time, percentage of video watched, and relate them to views and video properties such as length, category and topics. We propose a new metric, relative engagement, which is calibrated over video duration, stable over time, and strongly correlated with video quality. We leverage relative engagement to predict watch percentage before a video gains views, and can explain most of its variance - R2=0.77. We further link daily watch time to external sharing of a video using the self-exciting Hawkes Intensity Processes, and find that we can forecast daily watch time more accurately than daily views. We measure engagement over 5.3 million YouTube videos. This new dataset and benchmarks will be publicly available. This work provides a set of new yardsticks for measuring content including video and other length-constrained media such as songs and podcasts. It opens a new direction for modeling user-specific engagement and making better recommendations.
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