Forecasting online contents' popularity

Multimedia traffic over the Internet has boosted in the recent years and more media is watched and shared online. While being inpactful from many pespectives, predicting the interest generated by an online content is heterogeneous and constitutes a challenging task. Based on popularity patterns classification we suggest methods to improve any prediction model which uses training datasets. Through data driven evaluation on YouTube videos, we show that our methods perform promising results.