Discovering patterns of online popularity from time series
This work addresses the problem of predicting and analyzing online popularity for social media platforms, but it is incremental as it builds on existing time-series clustering methods with a new heuristic.
The authors tackled the problem of understanding how online content gains popularity by analyzing temporal patterns, using a new clustering algorithm on Twitter data to identify bursty and steady popularity behaviors, and found that the temporal pattern does not significantly affect final cumulative popularity.
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.