Emergent User Behavior on Twitter Modelled by a Stochastic Differential Equation

Data from the social-media site, Twitter, is used to study the fluctuations in tweet rates of brand names. The tweet rates are the result of a strongly correlated user behavior, which leads to bursty collective dynamics with a characteristic 1/f noise. Here we use the aggregated "user interest&...

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Detalles Bibliográficos
Autores principales: Mollgaard, Anders, Mathiesen, Joachim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4425543/
https://www.ncbi.nlm.nih.gov/pubmed/25955783
http://dx.doi.org/10.1371/journal.pone.0123876
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author Mollgaard, Anders
Mathiesen, Joachim
author_facet Mollgaard, Anders
Mathiesen, Joachim
author_sort Mollgaard, Anders
collection PubMed
description Data from the social-media site, Twitter, is used to study the fluctuations in tweet rates of brand names. The tweet rates are the result of a strongly correlated user behavior, which leads to bursty collective dynamics with a characteristic 1/f noise. Here we use the aggregated "user interest" in a brand name to model collective human dynamics by a stochastic differential equation with multiplicative noise. The model is supported by a detailed analysis of the tweet rate fluctuations and it reproduces both the exact bursty dynamics found in the data and the 1/f noise.
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spelling pubmed-44255432015-05-21 Emergent User Behavior on Twitter Modelled by a Stochastic Differential Equation Mollgaard, Anders Mathiesen, Joachim PLoS One Research Article Data from the social-media site, Twitter, is used to study the fluctuations in tweet rates of brand names. The tweet rates are the result of a strongly correlated user behavior, which leads to bursty collective dynamics with a characteristic 1/f noise. Here we use the aggregated "user interest" in a brand name to model collective human dynamics by a stochastic differential equation with multiplicative noise. The model is supported by a detailed analysis of the tweet rate fluctuations and it reproduces both the exact bursty dynamics found in the data and the 1/f noise. Public Library of Science 2015-05-08 /pmc/articles/PMC4425543/ /pubmed/25955783 http://dx.doi.org/10.1371/journal.pone.0123876 Text en © 2015 Mollgaard, Mathiesen http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Mollgaard, Anders
Mathiesen, Joachim
Emergent User Behavior on Twitter Modelled by a Stochastic Differential Equation
title Emergent User Behavior on Twitter Modelled by a Stochastic Differential Equation
title_full Emergent User Behavior on Twitter Modelled by a Stochastic Differential Equation
title_fullStr Emergent User Behavior on Twitter Modelled by a Stochastic Differential Equation
title_full_unstemmed Emergent User Behavior on Twitter Modelled by a Stochastic Differential Equation
title_short Emergent User Behavior on Twitter Modelled by a Stochastic Differential Equation
title_sort emergent user behavior on twitter modelled by a stochastic differential equation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4425543/
https://www.ncbi.nlm.nih.gov/pubmed/25955783
http://dx.doi.org/10.1371/journal.pone.0123876
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