  In this work, we propose a regression method to predict the popularity of an
online video based on temporal and visual cues. Our method uses Support Vector
Regression with Gaussian Radial Basis Functions. We show that modelling
popularity patterns with this approach provides higher and more stable
prediction results, mainly thanks to the non-linearity character of the
proposed method as well as its resistance against overfitting. We compare our
method with the state of the art on datasets containing over 14,000 videos from
YouTube and Facebook. Furthermore, we show that results obtained relying only
on the early distribution patterns, can be improved by adding social and visual
metadata.
