1. Predicting popularity dynamics of online contents using data filtering methods
- Author
-
Georges Linarès, Rachid El-Azouzi, Cedric Richier, Tania Jimenez, Eitan Altman, Jimenez, Tania, Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, Models for the performance analysis and the control of networks (MAESTRO), Inria Sophia Antipolis - Méditerranée (CRISAM), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
- Subjects
[INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM] ,Exploit ,Computer science ,Process (engineering) ,[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM] ,Baseline model ,020206 networking & telecommunications ,02 engineering and technology ,[INFO] Computer Science [cs] ,computer.software_genre ,Popularity ,[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation ,Data filtering ,Dynamics (music) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,[INFO]Computer Science [cs] ,Data mining ,[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation ,computer ,ComputingMilieux_MISCELLANEOUS - Abstract
This paper proposes a new prediction process to explain and predicts popularity evolution of YouTube videos. We exploit prior study on the classification of YouTube videos in order to predict the evolution of videos' view-count. This classification allows to identify important factors of the observed popularity dynamics. In particular, we use this classification as filtering method allowing to identify the factors responsible for this popularity evolution. Results given by extensive experiments show that the proposed prediction process is able to reduce the average prediction errors compared to a state-of-the-art baseline model. We also evaluate the impact of adding popularity criteria in the classification.
- Published
- 2016