Local Variation of Hashtag Spike Trains and Popularity in Twitter

We draw a parallel between hashtag time series and neuron spike trains. In each case, the process presents complex dynamic patterns including temporal correlations, burstiness, and all other types of nonstationarity. We propose the adoption of the so-called local variation in order to uncover salien...

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Detalles Bibliográficos
Autores principales: Sanlı, Ceyda, Lambiotte, Renaud
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/PMC4498919/
https://www.ncbi.nlm.nih.gov/pubmed/26161650
http://dx.doi.org/10.1371/journal.pone.0131704
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author Sanlı, Ceyda
Lambiotte, Renaud
author_facet Sanlı, Ceyda
Lambiotte, Renaud
author_sort Sanlı, Ceyda
collection PubMed
description We draw a parallel between hashtag time series and neuron spike trains. In each case, the process presents complex dynamic patterns including temporal correlations, burstiness, and all other types of nonstationarity. We propose the adoption of the so-called local variation in order to uncover salient dynamical properties, while properly detrending for the time-dependent features of a signal. The methodology is tested on both real and randomized hashtag spike trains, and identifies that popular hashtags present regular and so less bursty behavior, suggesting its potential use for predicting online popularity in social media.
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spelling pubmed-44989192015-07-17 Local Variation of Hashtag Spike Trains and Popularity in Twitter Sanlı, Ceyda Lambiotte, Renaud PLoS One Research Article We draw a parallel between hashtag time series and neuron spike trains. In each case, the process presents complex dynamic patterns including temporal correlations, burstiness, and all other types of nonstationarity. We propose the adoption of the so-called local variation in order to uncover salient dynamical properties, while properly detrending for the time-dependent features of a signal. The methodology is tested on both real and randomized hashtag spike trains, and identifies that popular hashtags present regular and so less bursty behavior, suggesting its potential use for predicting online popularity in social media. Public Library of Science 2015-07-10 /pmc/articles/PMC4498919/ /pubmed/26161650 http://dx.doi.org/10.1371/journal.pone.0131704 Text en © 2015 Sanlı, Lambiotte 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
Sanlı, Ceyda
Lambiotte, Renaud
Local Variation of Hashtag Spike Trains and Popularity in Twitter
title Local Variation of Hashtag Spike Trains and Popularity in Twitter
title_full Local Variation of Hashtag Spike Trains and Popularity in Twitter
title_fullStr Local Variation of Hashtag Spike Trains and Popularity in Twitter
title_full_unstemmed Local Variation of Hashtag Spike Trains and Popularity in Twitter
title_short Local Variation of Hashtag Spike Trains and Popularity in Twitter
title_sort local variation of hashtag spike trains and popularity in twitter
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4498919/
https://www.ncbi.nlm.nih.gov/pubmed/26161650
http://dx.doi.org/10.1371/journal.pone.0131704
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