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Revisiting Popularity Characterization and Modeling for User-generated Videos
University of Saskatchewan, Canada.
University of Saskatchewan, Canada.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
NICTA, Alexandria, NSW, Australia.
2013 (English)In: Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), 2013 IEEE 21st International Symposium, IEEE , 2013, 350-354 p.Conference paper (Refereed)
Place, publisher, year, edition, pages
IEEE , 2013. 350-354 p.
, Proceedings of the International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, ISSN 1526-7539
Keyword [en]
Video popularity dynamics; User generated content
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-105280DOI: 10.1109/MASCOTS.2013.50OAI: diva2:705301
IEEE 21st International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), 14-16 August 2013, San Francisco, CA, USA

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.

Available from: 2014-03-15 Created: 2014-03-15 Last updated: 2015-05-28Bibliographically approved

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