Exploratory Analysis of Twitter data: Understanding the health information connections during the Yellow Fever Outbreak in 2017
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Brazilian Journal of Information Science |
Texto Completo: | https://revistas.marilia.unesp.br/index.php/bjis/article/view/10179 |
Resumo: | This paper presents a detailed analysis of how health information was shared and discussed on Twitter in terms of awareness and opinions during the 2017 Yellow Fever outbreak in Brazil. For this, the data mining approach with exploratory graph analysis was performed. As main results, Twitter activity peaks were identified compared to peaks of cases reported in some regions of the country, an analysis of hashtags linked to the main subject and different topics from the exploratory analysis of graphs such as vaccination campaign, feelings, prevention, rumors, other diseases linked to the same transmitter, among others. This study illustrates that social networks, such as Twitter, offer unique opportunities for participatory surveillance, which can assist in monitoring some aspects of public health and offer additional data to health managers on how people interact during an outbreak. |
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Brazilian Journal of Information Science |
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Exploratory Analysis of Twitter data: Understanding the health information connections during the Yellow Fever Outbreak in 2017Análisis exploratorio de datos de Twitter: Comprender las conexiones de información de salud durante el brote de fiebre amarilla en 2017Análise exploratória de dados do Twitter: compreendendo as conexões da informação de saúde durante o surto da febre amarela em 2017Redes SociaisMineração de DadosFebre AmarelaAnálise Exploratória de GrafosRedes socialesProcesamiento de datosFiebre amarillaAnálisis Exploratorio de GráficosSocial NetworkData MiningYellow FeverExploratory Graph AnalysisThis paper presents a detailed analysis of how health information was shared and discussed on Twitter in terms of awareness and opinions during the 2017 Yellow Fever outbreak in Brazil. For this, the data mining approach with exploratory graph analysis was performed. As main results, Twitter activity peaks were identified compared to peaks of cases reported in some regions of the country, an analysis of hashtags linked to the main subject and different topics from the exploratory analysis of graphs such as vaccination campaign, feelings, prevention, rumors, other diseases linked to the same transmitter, among others. This study illustrates that social networks, such as Twitter, offer unique opportunities for participatory surveillance, which can assist in monitoring some aspects of public health and offer additional data to health managers on how people interact during an outbreak.Este artículo presenta un análisis exploratorio de cómo se compartió y discutió la información de salud en Twitter en términos de temas de conciencia y posiciones durante el brote de fiebre amarilla de 2017 en Brasil. Para esto, se utilizó el enfoque de minería de datos con análisis de gráficos exploratorios. Como resultados principales, se identificaron picos de mensajes en comparación con los picos de casos reportados en algunas regiones del país, un análisis de hashtags vinculados al tema principal y diferentes temas del análisis exploratorio de gráficos como campaña de vacunación, sentimientos, prevención, rumores, otras enfermedades vinculadas al mismo transmisor, entre otras. Este estudio demostró que las redes sociales, como Twitter, ofrecen oportunidades únicas para la vigilancia participativa, que puede ayudar a monitorear algunos aspectos de la salud pública y ofrecer datos adicionales a los administradores de salud sobre cómo las personas interactúan durante un brote.Este artigo apresenta uma análise exploratória de como as informações de saúde foram compartilhadas e discutidas no Twitter em termos de tópicos de conscientização e posicionamentos durante surto da Febre Amarela de 2017 no Brasil. Para isso, foi utilizada a abordagem de mineração de dados com análise exploratória de grafos. Como principais resultados, foram identificados os picos de mensagens comparados aos picos de casos notificados em algumas regiões do país, uma análise das hashtags vinculadas ao principal assunto e diferentes tópicos oriundos da análise exploratória de grafos como campanha de vacinação, sentimentos, prevenção, rumores, outras doenças vinculadas ao mesmo transmissor entre outros. Este estudo demonstrou que as redes sociais, como o Twitter, oferecem oportunidades únicas para a vigilância participativa, podendo auxiliar no monitoramento de alguns aspectos da saúde pública e oferecer dados adicionais aos gestores de saúde de como as pessoas interagem durante um surto.Faculdade de Filosofia e Ciências2020-08-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.marilia.unesp.br/index.php/bjis/article/view/1017910.36311/1940-1640.2020.v14n3.10179Brazilian Journal of Information Science: Research Trends; Vol. 14 No. 3 - jul-set (2020); e020006Brazilian Journal of Information Science: Research Trends; Vol. 14 Núm. 3 - jul-set (2020); e020006Brazilian Journal of Information Science: research trends; v. 14 n. 3 - jul-set (2020); e0200061981-1640reponame:Brazilian Journal of Information Scienceinstname:Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP)instacron:UNESPporhttps://revistas.marilia.unesp.br/index.php/bjis/article/view/10179/6731Copyright (c) 2020 Gabriela Denise de Araujo, Fabricio Landi de Moraes, Ivan Torres Pisainfo:eu-repo/semantics/openAccessAraujo, Gabriela Denise deMoraes, Fabricio Landi dePisa, Ivan Torres2022-01-04T19:40:50Zoai:ojs.www2.marilia.unesp.br:article/10179Revistahttps://revistas.marilia.unesp.br/index.php/bjis/indexPUBhttps://revistas.marilia.unesp.br/index.php/bjis/oaibrajis.marilia@unesp.br||1981-16401981-1640opendoar:2022-01-04T19:40:50Brazilian Journal of Information Science - Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP)false |
dc.title.none.fl_str_mv |
Exploratory Analysis of Twitter data: Understanding the health information connections during the Yellow Fever Outbreak in 2017 Análisis exploratorio de datos de Twitter: Comprender las conexiones de información de salud durante el brote de fiebre amarilla en 2017 Análise exploratória de dados do Twitter: compreendendo as conexões da informação de saúde durante o surto da febre amarela em 2017 |
title |
Exploratory Analysis of Twitter data: Understanding the health information connections during the Yellow Fever Outbreak in 2017 |
spellingShingle |
Exploratory Analysis of Twitter data: Understanding the health information connections during the Yellow Fever Outbreak in 2017 Araujo, Gabriela Denise de Redes Sociais Mineração de Dados Febre Amarela Análise Exploratória de Grafos Redes sociales Procesamiento de datos Fiebre amarilla Análisis Exploratorio de Gráficos Social Network Data Mining Yellow Fever Exploratory Graph Analysis |
title_short |
Exploratory Analysis of Twitter data: Understanding the health information connections during the Yellow Fever Outbreak in 2017 |
title_full |
Exploratory Analysis of Twitter data: Understanding the health information connections during the Yellow Fever Outbreak in 2017 |
title_fullStr |
Exploratory Analysis of Twitter data: Understanding the health information connections during the Yellow Fever Outbreak in 2017 |
title_full_unstemmed |
Exploratory Analysis of Twitter data: Understanding the health information connections during the Yellow Fever Outbreak in 2017 |
title_sort |
Exploratory Analysis of Twitter data: Understanding the health information connections during the Yellow Fever Outbreak in 2017 |
author |
Araujo, Gabriela Denise de |
author_facet |
Araujo, Gabriela Denise de Moraes, Fabricio Landi de Pisa, Ivan Torres |
author_role |
author |
author2 |
Moraes, Fabricio Landi de Pisa, Ivan Torres |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Araujo, Gabriela Denise de Moraes, Fabricio Landi de Pisa, Ivan Torres |
dc.subject.por.fl_str_mv |
Redes Sociais Mineração de Dados Febre Amarela Análise Exploratória de Grafos Redes sociales Procesamiento de datos Fiebre amarilla Análisis Exploratorio de Gráficos Social Network Data Mining Yellow Fever Exploratory Graph Analysis |
topic |
Redes Sociais Mineração de Dados Febre Amarela Análise Exploratória de Grafos Redes sociales Procesamiento de datos Fiebre amarilla Análisis Exploratorio de Gráficos Social Network Data Mining Yellow Fever Exploratory Graph Analysis |
description |
This paper presents a detailed analysis of how health information was shared and discussed on Twitter in terms of awareness and opinions during the 2017 Yellow Fever outbreak in Brazil. For this, the data mining approach with exploratory graph analysis was performed. As main results, Twitter activity peaks were identified compared to peaks of cases reported in some regions of the country, an analysis of hashtags linked to the main subject and different topics from the exploratory analysis of graphs such as vaccination campaign, feelings, prevention, rumors, other diseases linked to the same transmitter, among others. This study illustrates that social networks, such as Twitter, offer unique opportunities for participatory surveillance, which can assist in monitoring some aspects of public health and offer additional data to health managers on how people interact during an outbreak. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-08-28 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://revistas.marilia.unesp.br/index.php/bjis/article/view/10179 10.36311/1940-1640.2020.v14n3.10179 |
url |
https://revistas.marilia.unesp.br/index.php/bjis/article/view/10179 |
identifier_str_mv |
10.36311/1940-1640.2020.v14n3.10179 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://revistas.marilia.unesp.br/index.php/bjis/article/view/10179/6731 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2020 Gabriela Denise de Araujo, Fabricio Landi de Moraes, Ivan Torres Pisa info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2020 Gabriela Denise de Araujo, Fabricio Landi de Moraes, Ivan Torres Pisa |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Faculdade de Filosofia e Ciências |
publisher.none.fl_str_mv |
Faculdade de Filosofia e Ciências |
dc.source.none.fl_str_mv |
Brazilian Journal of Information Science: Research Trends; Vol. 14 No. 3 - jul-set (2020); e020006 Brazilian Journal of Information Science: Research Trends; Vol. 14 Núm. 3 - jul-set (2020); e020006 Brazilian Journal of Information Science: research trends; v. 14 n. 3 - jul-set (2020); e020006 1981-1640 reponame:Brazilian Journal of Information Science instname:Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Brazilian Journal of Information Science |
collection |
Brazilian Journal of Information Science |
repository.name.fl_str_mv |
Brazilian Journal of Information Science - Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP) |
repository.mail.fl_str_mv |
brajis.marilia@unesp.br|| |
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1754840471785439232 |