Predicting popularity of online videos using Support Vector Regression
This work addresses the problem of popularity prediction for online video platforms, but it is incremental as it applies an existing method to a specific domain.
The authors tackled the problem of predicting online video popularity by using Support Vector Regression with Gaussian Radial Basis Functions, achieving higher and more stable prediction results compared to state-of-the-art methods on datasets of over 14,000 videos from YouTube and Facebook.
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.