Modeling the Evolution of User-generated Content on a Large Video Sharing Platform

Abstract

Video sharing and entertainment websites have rapidly grown in popularity and now constitute some of the most visited websites on the Internet. Despite the high usage and user engagement, most of recent research on online media platforms have restricted themselves to networking based social media sites like Facebook or Twitter. The current study is among the first to perform a large-scale empirical study using longitudinal video upload data from one of the largest online video sites. Unlike previous studies in the online media space that have focused exclusively on demand-side research questions, we model the supply-side of the crowd contributed video ecosystem on this platform. The modeling and subsequent prediction of video uploads is made complicated by the heterogeneity of video types (e.g. popular vs. niche video genres), and the inherent time trend effects. We identify distinct genre-clusters from our dataset and employ a self-exciting Hawkes point-process model on each of these clusters to fully specify and estimate the video upload process. Our findings show that using a relatively parsimonious point-process model, we are able to achieve higher model fit, and predict video uploads to the platform with a higher accuracy than competing models.