Predicting popularity dynamics of online contents using data filtering methods
Cédric Richier, Rachid El-Azouzi, Tania Jiménez, Eitan Altman, Georges Linarès · 2016
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.