A Vocabulary for Growth: Topic Modeling of Content Popularity Evolution

In this paper, we present a novel method to predict the long-term popularity of user-generated content (UGC). At first, the method clusters the dynamics of UGC popularity into a vocabulary of growth in popularity (sequence) by using a mixture model. Eventually, the method assigns to each sequence a topic model to describe the dynamics of the sequence in a compact way. We then use this topic model to identify similar patterns of growth in popularity of newly observed UGC. The proposed method has two key features: First, it considers the historical dynamics of the UGC popularity, and second it provides long-term popularity prediction. Results on the real dataset of UGC show that the proposed method is flexible, and able to accurately forecast the complete growth in popularity of a given UGC.