Dynamiques des popularités dans YouTube

RESUME. Cet article est une etude de l’evolution du nombre de vues des contenus dans YouTube. Nous proposons dans un premier temps plusieurs modeles inspires de l’economie et de la biologie pour caracteriser les courbes d’evolution des nombres de vues des videos. Dans un deuxieme temps, nous proposons une methode automatique de classification de ces courbes en les associant a l’un des differents modeles suggeres. Nous montrons, sur un large ensemble de donnees, que 90% des videos peuvent etre associees a l’un de ces modeles avec une erreur moyenne inferieure a 5%. Une etude empirique est menee au sujet de l’impact de la popularite et des categories de videos sur l’evolution des nombres de vues. Enfin, cette classification est utilisee dans un exemple de methode de prediction de la popularite des videos.

[1]  Vijay Mahajan,et al.  Chapter 8 New-product diffusion models , 1993, Marketing.

[2]  F. Bass The Relationship between Diffusion Rates, Experience Curves, and Demand Elasticities for Consumer Durable Technological Innovations , 1980 .

[3]  N. Ling The Mathematical Theory of Infectious Diseases and its applications , 1978 .

[4]  L. Meyers Contact network epidemiology: Bond percolation applied to infectious disease prediction and control , 2006 .

[5]  Jiangchuan Liu,et al.  Statistics and Social Network of YouTube Videos , 2008, 2008 16th Interntional Workshop on Quality of Service.

[6]  Didier Sornette,et al.  Viral, Quality, and Junk Videos on YouTube: Separating Content from Noise in an Information-Rich Environment , 2008, AAAI Spring Symposium: Social Information Processing.

[7]  W. Edwards Deming The Chi-Test and Curve Fitting , 1934 .

[8]  Santo Fortunato,et al.  Traffic in Social Media II: Modeling Bursty Popularity , 2010, 2010 IEEE Second International Conference on Social Computing.

[9]  Vijay Mahajan,et al.  New-Product Diffusion Models (International Series in Quantitative Marketing, Vol. 11) , 2000 .

[10]  Zongpeng Li,et al.  Youtube traffic characterization: a view from the edge , 2007, IMC '07.

[11]  Francesco De Pellegrini,et al.  YOUStatAnalyzer: a tool for analysing the dynamics of YouTube content popularity , 2013, VALUETOOLS.

[12]  Christos Faloutsos,et al.  Epidemic thresholds in real networks , 2008, TSEC.

[13]  Yong-Yeol Ahn,et al.  Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems , 2009, IEEE/ACM Transactions on Networking.

[14]  Alexander Grey,et al.  The Mathematical Theory of Infectious Diseases and Its Applications , 1977 .

[15]  Pablo Rodriguez,et al.  I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system , 2007, IMC '07.

[16]  Donald F. Towsley,et al.  The effect of network topology on the spread of epidemics , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[17]  Michalis Faloutsos,et al.  A First Step Towards Understanding Popularity in YouTube , 2010, 2010 INFOCOM IEEE Conference on Computer Communications Workshops.

[18]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[19]  Niklas Carlsson,et al.  Characterizing web-based video sharing workloads , 2009, WWW '09.

[20]  Bernardo A. Huberman,et al.  Predicting the popularity of online content , 2008, Commun. ACM.