TY - JOUR
T1 - Discovering patterns of online popularity from time series
AU - Ozer, M
AU - SAPIENZA, Anna
AU - Abeliuk, A
AU - Muric, G
AU - Ferrara, E
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - https://iris.uniupo.it/handle/11579/181323
U2 - 10.1016/j.eswa.2020.113337
DO - 10.1016/j.eswa.2020.113337
M3 - Article
SN - 0957-4174
JO - Expert Systems with Applications
JF - Expert Systems with Applications
ER -