Abstract
This paper presents new results on characterization and modeling of user-generated video popularity evolution, based on a recent complementary data collection for videos that were previously the subject of an eight month data collection campaign during 2008/09. In particular, during 2011, we collected two contiguous months of weekly view counts for videos in two separate 2008/09 datasets, namely the ``recently-uploaded'' and the ``keyword-search'' datasets. These datasets contain statistics for videos that were uploaded within 7 days of the start of data collection in 2008 and videos that were discovered using a keyword search algorithm in 2008, respectively. Our analysis shows that the average weekly view count for the recently-uploaded videos had not decreased by the time of the second measurement period, in comparison to the middle and later portions of the first measurement period. The new data is used to evaluate the accuracy of a previously proposed model for synthetic view count generation for time periods that are substantially longer than previously considered. We find that the model yielded distributions of total (lifetime) video view counts that match the empirical distributions, however, significant differences between the model and empirical data were observed with respect to other metrics. These differences appear to arise because of particular popularity characteristics that change over time rather than being week-invariant as assumed in the model.