Predicting popularity via a generative model with adaptive peeking window

Predicting the popularity of online content is an important issue for uncovering rules governing collective human behaviors in networks. Also, content popularity prediction finds application in an array of areas. In this paper, we propose a generative approach using a Hawkes process, the purpose of which is to predict the eventual popularity of online content from the observed initial period of the information cascade. Our approach is distinguished from existing approaches via its ability to explore an adaptive peeking window and its added remarkable predictive power that was inherited from the feature-driven method. Further, we proposed a procedure for exploring an adaptive peeking window, which allows us to answer the question, “How can we obtain the most effective part of the history to make an accurate prediction?” And the added predictive layer bridges the gap between the generative method and the feature-driven method. Empirical studies on real world datasets demonstrate that the proposed method significantly outperformed the existing approaches.