  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 multi-faceted temporal analysis of the evolution
of popular online contents. Here, we present dipm-SC: a multi-dimensional
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 a
real-world Twitter dataset. By clustering the multidimensional time-series of
the popularity of contents coupled with other domain-specific dimensions, we
uncover two main patterns of popularity: bursty and steady temporal behaviors.
Moreover, we find that the way popularity is gained over time has no
significant impact on the final cumulative popularity.
