Discovering patterns of online popularity from time series

How is popularity gained online? Is being successful strictly related to rapidly becoming viral in an online platform, or is it possible to acquire popularity in a steady and disciplined fashion? What are other temporal characteristics that can unveil the popularity of online content? To answer these questions, we leverage a multifaceted temporal analysis of the evolution of popular online content. We present dipm-SC: a multidimensional shape-based time-series clustering algorithm with a heuristic to find the optimal number of clusters. First, we validate the accuracy of our algorithm on synthetic datasets generated from benchmark time series models. Second, we show that dipm-SC can uncover meaningful clusters of popularity behaviors in real-world GitHub and Twitter datasets. By clustering the multidimensional time-series of the popularity of contents coupled with other domain-specific dimensions, we discover two main patterns of popularity: bursty and steady temporal behaviors. Furthermore, we find that the way popularity is gained over time has no significant impact on the final cumulative popularity.