  The goal of this paper is to study the behaviour of view-count in YouTube. We
first propose several bio-inspired models for the evolution of the view-count
of YouTube videos. We show, using a large set of empirical data, that the
view-count 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 view-count 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
view-count. We finally use the above classification along with the automatic
parameters extraction in order to predict the evolution of videos' view-count.
