Abstract
This paper presents Pop-Forecast, a systematic method for accurately forecasting the popularity of videos promoted through social networks. Pop-Forecast aims to optimize the forecasting accuracy and the timeliness with which forecasts are issued, by explicitly taking into account the dynamic propagation of videos in social networks. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the performance loss of Pop-Forecast as compared to that obtained by an omniscient oracle and prove that the bound is sublinear in the number of video arrivals, thereby guaranteeing its fast rate of convergence as well as its asymptotic convergence to the optimal performance. We validate the performance of Pop-Forecast through extensive experiments using real-world data traces collected from the videos shared in RenRen, one of the largest online social networks in China. These experiments show that our proposed method outperforms existing approaches for popularity prediction (which do not take into account the propagation in social network) by more than 30% in terms of prediction rewards.