2014/04/09 by Cédric Richier, Richier, Cédric, Eitan Altman +11 · 2 citations
Computer Science · Physics and Astronomy · #Caching and Content Delivery #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Peer-to-Peer Network Technologies #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1404.2570
Technical report, 10 pages. Added MER definition analysis. Added interval confidence intervals. Added prediction results
openalex publication_date 2014/04/09 · arxiv created 2014/05/28 · arxiv updated 2014/05/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.