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

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 ev...

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Detalles Bibliográficos
Autores principales: Fujiyama, Toshifumi, Matsui, Chihiro, Takemura, Akimichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4978406/
https://www.ncbi.nlm.nih.gov/pubmed/27505155
http://dx.doi.org/10.1371/journal.pone.0160592
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author Fujiyama, Toshifumi
Matsui, Chihiro
Takemura, Akimichi
author_facet Fujiyama, Toshifumi
Matsui, Chihiro
Takemura, Akimichi
author_sort Fujiyama, Toshifumi
collection PubMed
description 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.
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spelling pubmed-49784062016-08-25 A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services Fujiyama, Toshifumi Matsui, Chihiro Takemura, Akimichi PLoS One Research Article 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. Public Library of Science 2016-08-09 /pmc/articles/PMC4978406/ /pubmed/27505155 http://dx.doi.org/10.1371/journal.pone.0160592 Text en © 2016 Fujiyama et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Fujiyama, Toshifumi
Matsui, Chihiro
Takemura, Akimichi
A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services
title A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services
title_full A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services
title_fullStr A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services
title_full_unstemmed A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services
title_short A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services
title_sort power-law growth and decay model with autocorrelation for posting data to social networking services
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4978406/
https://www.ncbi.nlm.nih.gov/pubmed/27505155
http://dx.doi.org/10.1371/journal.pone.0160592
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