Using centrality measures to improve the classification performance of tweets during natural disasters

ABSTRACT Online social networks like Twitter facilitate instant communication during natural disasters. A key problem is to distinguish in real-time the most assertive and contingent tweets related to the current disaster from the whole streaming. To address this problem, machine learning allows to...

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Autores principales:	Vásquez,Rodrigo, Riquelme,Fabián, González-Cantergiani,Pablo, Vásquez,Cristobal
Lenguaje:	English
Publicado:	Universidad de Tarapacá. 2021
Materias:	Active learning Twitter centrality measure disaster response user influence
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052021000100073
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spelling	oai:scielo:S0718-330520210001000732021-04-15Using centrality measures to improve the classification performance of tweets during natural disastersVásquez,RodrigoRiquelme,FabiánGonzález-Cantergiani,PabloVásquez,Cristobal Active learning Twitter centrality measure disaster response user influence ABSTRACT Online social networks like Twitter facilitate instant communication during natural disasters. A key problem is to distinguish in real-time the most assertive and contingent tweets related to the current disaster from the whole streaming. To address this problem, machine learning allows to classify tweets according to their relevance or credibility. In this article, it is proposed to use centrality measures to improve the training data sample of active learning classifiers. As a case study, tweets collected during the massive floods in Santiago of Chile at 2016 are considered. This approach improves the consistency and pertinence of the labeling process, as well as the classifiers' performance.info:eu-repo/semantics/openAccessUniversidad de Tarapacá.Ingeniare. Revista chilena de ingeniería v.29 n.1 20212021-03-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052021000100073en10.4067/S0718-33052021000100073
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Active learning Twitter centrality measure disaster response user influence
spellingShingle	Active learning Twitter centrality measure disaster response user influence Vásquez,Rodrigo Riquelme,Fabián González-Cantergiani,Pablo Vásquez,Cristobal Using centrality measures to improve the classification performance of tweets during natural disasters
description	ABSTRACT Online social networks like Twitter facilitate instant communication during natural disasters. A key problem is to distinguish in real-time the most assertive and contingent tweets related to the current disaster from the whole streaming. To address this problem, machine learning allows to classify tweets according to their relevance or credibility. In this article, it is proposed to use centrality measures to improve the training data sample of active learning classifiers. As a case study, tweets collected during the massive floods in Santiago of Chile at 2016 are considered. This approach improves the consistency and pertinence of the labeling process, as well as the classifiers' performance.
author	Vásquez,Rodrigo Riquelme,Fabián González-Cantergiani,Pablo Vásquez,Cristobal
author_facet	Vásquez,Rodrigo Riquelme,Fabián González-Cantergiani,Pablo Vásquez,Cristobal
author_sort	Vásquez,Rodrigo
title	Using centrality measures to improve the classification performance of tweets during natural disasters
title_short	Using centrality measures to improve the classification performance of tweets during natural disasters
title_full	Using centrality measures to improve the classification performance of tweets during natural disasters
title_fullStr	Using centrality measures to improve the classification performance of tweets during natural disasters
title_full_unstemmed	Using centrality measures to improve the classification performance of tweets during natural disasters
title_sort	using centrality measures to improve the classification performance of tweets during natural disasters
publisher	Universidad de Tarapacá.
publishDate	2021
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052021000100073
work_keys_str_mv	AT vasquezrodrigo usingcentralitymeasurestoimprovetheclassificationperformanceoftweetsduringnaturaldisasters AT riquelmefabian usingcentralitymeasurestoimprovetheclassificationperformanceoftweetsduringnaturaldisasters AT gonzalezcantergianipablo usingcentralitymeasurestoimprovetheclassificationperformanceoftweetsduringnaturaldisasters AT vasquezcristobal usingcentralitymeasurestoimprovetheclassificationperformanceoftweetsduringnaturaldisasters
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Using centrality measures to improve the classification performance of tweets during natural disasters

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