Bio-Inspired Models for Characterizing YouTube Viewcount

Abstract : The goal of this paper is to study the behaviour of viewcount in YouTube. We first propose several bio-inspired models for the evolution of the viewcount of YouTube videos. We show, using a large set of empirical data, that the viewcount for 90% of videos in YouTube can indeed be associated to at least one of these models, with a Mean Error which does not exceed 5%. We derive automatic ways of classifying the viewcount curve into one of these models and of extracting the most suitable parameters of the model. We study empirically the impact of videos' popularity and category on the evolution of its viewcount. We finally use the above classification along with the automatic parameters extraction in order to predict the evolution of videos' viewcount.
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Communication dans un congrès
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), Aug 2014, Beijing, China. pp.297-305, 2014, 〈10.1109/ASONAM.2014.6921600 〉
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Cedric Richier, Eitan Altman, Rachid El-Azouzi, Tania Jimenez, Georges Linarès, et al.. Bio-Inspired Models for Characterizing YouTube Viewcount. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), Aug 2014, Beijing, China. pp.297-305, 2014, 〈10.1109/ASONAM.2014.6921600 〉. 〈hal-01011069〉

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