  Video sharing and entertainment websites have rapidly grown in popularity and
now constitute some of the most visited websites on the Internet. Despite the
active user engagement on these online video-sharing platforms, most of recent
research on online media platforms have restricted themselves to networking
based social media sites, like Facebook or Twitter. We depart from previous
studies in the online media space that have focused exclusively on demand-side
user engagement, by modeling the supply-side of the crowd-contributed videos on
this platform. The current study is among the first to perform a large-scale
empirical study using longitudinal video upload data from a large online video
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 associated with media uploads. 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. Additionally, we go beyond prediction to
disentangle potential factors that govern user engagement and determine the
video upload rates, which improves our analysis with additional explanatory
power. 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. 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.
