User Generated Content (UGC) sites like YouTube are nowadays entertaining over a billion people. Identifying popular contents is essential for these giant UGC sites as they allow users to request contents from a potentially unlimited selection in an asynchronous fashion. In this work, we conduct an analysis on the popularity prediction problem in UGC sites and complement previous work with two new aspects, namely differentiating contents that attract a lot of attention and that users really appreciate, and leveraging built-in social features to predict the content popularity immediately upon publication.

To this end, we conduct an extensive measurement and analysis of BiliBili, a YouTube-like UGC site with enhanced social features including user following, chat replay, and virtual money donation. Based on datasets that contain over 2 million videos and over 28 million users, we characterize the video repository and the user activities, we analyze the video popularities, we propose graph models that reveal user relationships and high-level social structures, and we successfully apply our findings to build machine-learned classifiers to identify popular videos.