Characterizing and Predicting Supply-side Engagement on Video Sharing Platforms Using a Hawkes Process Model

Abstract

Video sharing platforms are one of the most popular and engaging platforms on the Internet today. Despite the increasing levels of user activity on these video platforms, current research on digital platforms have largely focused on social media and networking websites like Facebook and Twitter. We depart from previous work that have focused primarily on user demands (i.e. activity of viewers), and instead focus our attention to the supply-side activities on the platform (i.e. activity of video uploaders). We perform a large-scale empirical study by leveraging longitudinal video upload data from a major online video platform, demonstrating (i) heterogeneity of video types (e.g. presence of popular vs. niche genres), and (ii) inherent seasonality effects associated with video uploads. Through our analyses, we uncover a set of informative genre-clusters and estimate a self-exciting Hawkes point-process model on each of these clusters, to fully specify and estimate the video upload process. Additionally, we disentangle potential factors that govern user engagement and determine the video upload rates, which help supplement our analysis with additional explanatory power. Our results emphasize that using a parsimonious and relatively simple point-process model, we were able to obtain a high model fit, as well as perform prediction of video upload volumes with a higher accuracy than a number of competing models. The findings from this study can benefit platform owners in better understanding how their supply-side users engage with their site over time. We also offer a robust method for performing media upload prediction that is likely to be generalizable across media platforms which demonstrate similar temporal and genre-level heterogeneity.