title: A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services.
abstract: We propose a power-law growth and decay model for posting data to social networking services before and after social events. We model the time series structure of deviations from the power-law growth and decay with a conditional Poisson autoregressive (AR) model. Online postings related to social events are described by five parameters in the power-law growth and decay model, each of which characterizes different aspects of interest in the event. We assess the validity of parameter estimates in terms of confidence intervals, and compare various submodels based on likelihoods and information criteria.
authors: Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
authors: Toshifumi
authors: T
authors: Fujiyama
authors: Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
authors: Chihiro
authors: C
authors: Matsui
authors: The Center for Data Science Edutation and Research, Shiga University, Shiga, Japan.
authors: Akimichi
authors: A
authors: Takemura
mesh_terms: D015233:Models, Statistical; D016012:Poisson Distribution; D060756:Social Networking; D013223:Statistics as Topic / Q000379:methods*