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A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services

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  • Toshifumi Fujiyama
  • Chihiro Matsui
  • Akimichi Takemura

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.

Suggested Citation

  • Toshifumi Fujiyama & Chihiro Matsui & Akimichi Takemura, 2016. "A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-14, August.
    • Handle: RePEc:plo:pone00:0160592
    DOI: 10.1371/journal.pone.0160592
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