Serveur d'exploration MERS

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Twitter and Middle East respiratory syndrome, South Korea, 2015: A multi-lingual study.

Identifieur interne : 000730 ( PubMed/Checkpoint ); précédent : 000729; suivant : 000731

Twitter and Middle East respiratory syndrome, South Korea, 2015: A multi-lingual study.

Auteurs : Isaac Chun-Hai Fung [États-Unis] ; Jing Zeng [Australie] ; Chung-Hong Chan [Hong Kong] ; Hai Liang [Hong Kong] ; Jingjing Yin [États-Unis] ; Zhaochong Liu [États-Unis] ; Zion Tsz Ho Tse [États-Unis] ; King-Wa Fu [Hong Kong]

Source :

RBID : pubmed:30479298

Descripteurs français

English descriptors

Abstract

Different linguo-cultural communities might react to an outbreak differently. The 2015 South Korean MERS outbreak presented an opportunity for us to compare tweets responding to the same outbreak in different languages.

DOI: 10.1016/j.idh.2017.08.005
PubMed: 30479298


Affiliations:


Links toward previous steps (curation, corpus...)


Links to Exploration step

pubmed:30479298

Le document en format XML

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<term>Coronavirus Infections (epidemiology)</term>
<term>Coronavirus Infections (prevention & control)</term>
<term>Cross-Sectional Studies</term>
<term>Cultural Characteristics</term>
<term>Disease Outbreaks</term>
<term>Humans</term>
<term>Language</term>
<term>Republic of Korea (epidemiology)</term>
<term>Social Media</term>
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<term>Caractéristiques culturelles</term>
<term>Flambées de maladies</term>
<term>Humains</term>
<term>Infections à coronavirus ()</term>
<term>Infections à coronavirus (épidémiologie)</term>
<term>Langage</term>
<term>Médias sociaux</term>
<term>République de Corée (épidémiologie)</term>
<term>Études transversales</term>
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<term>Republic of Korea</term>
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<keywords scheme="MESH" qualifier="epidemiology" xml:lang="en">
<term>Coronavirus Infections</term>
</keywords>
<keywords scheme="MESH" qualifier="prevention & control" xml:lang="en">
<term>Coronavirus Infections</term>
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<term>Infections à coronavirus</term>
<term>République de Corée</term>
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<term>Cross-Sectional Studies</term>
<term>Cultural Characteristics</term>
<term>Disease Outbreaks</term>
<term>Humans</term>
<term>Language</term>
<term>Social Media</term>
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<term>Caractéristiques culturelles</term>
<term>Flambées de maladies</term>
<term>Humains</term>
<term>Infections à coronavirus</term>
<term>Langage</term>
<term>Médias sociaux</term>
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<div type="abstract" xml:lang="en">Different linguo-cultural communities might react to an outbreak differently. The 2015 South Korean MERS outbreak presented an opportunity for us to compare tweets responding to the same outbreak in different languages.</div>
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<DateCompleted>
<Year>2019</Year>
<Month>06</Month>
<Day>19</Day>
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<DateRevised>
<Year>2019</Year>
<Month>06</Month>
<Day>19</Day>
</DateRevised>
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<ISSN IssnType="Electronic">2468-0869</ISSN>
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<Volume>23</Volume>
<Issue>1</Issue>
<PubDate>
<Year>2018</Year>
<Month>03</Month>
</PubDate>
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<Title>Infection, disease & health</Title>
<ISOAbbreviation>Infect Dis Health</ISOAbbreviation>
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<ArticleTitle>Twitter and Middle East respiratory syndrome, South Korea, 2015: A multi-lingual study.</ArticleTitle>
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<AbstractText Label="BACKGROUND">Different linguo-cultural communities might react to an outbreak differently. The 2015 South Korean MERS outbreak presented an opportunity for us to compare tweets responding to the same outbreak in different languages.</AbstractText>
<AbstractText Label="METHODS">We obtained a 1% sample through Twitter streaming application programming interface from June 1 to 30, 2015. We identified MERS-related tweets with keywords such as 'MERS' and its translation in five different languages. We translated non-English tweets into English for statistical comparison.</AbstractText>
<AbstractText Label="RESULTS">We retrieved MERS-related Twitter data in five languages: Korean (N = 21,823), English (N = 4024), Thai (N = 2084), Japanese (N = 1334) and Indonesian (N = 1256). Categories of randomly selected user profiles (p < 0.001) and the top 30 sources of retweets (p < 0.001) differed between the five language corpora. Among the randomly selected user profiles, K-pop fans ranged from 4% in the Korean corpus to 70% in the Thai corpus; media ranged from 0% (Thai) to 14% (Indonesian); political advocates ranged from 0% (Thai) to 19% (Japanese); medical professionals ranged from 0% (Thai) to 7% (English). Among the top 30 sources of retweets for each corpus (150 in total), 70 (46.7%) were media; 29 (19.3%) were K-pop fans; 7 (4.7%) were political; 9 (6%) were medical; and 35 (23.3%) were categorized as 'Others'. We performed chi-square feature selection and identified the top 20 keywords that were most unique to each corpus.</AbstractText>
<AbstractText Label="CONCLUSION">Different linguo-cultural communities exist on Twitter and they might react to the same outbreak differently. Understanding audiences' unique Twitter cultures will allow public health agencies to develop appropriate Twitter health communication strategies.</AbstractText>
<CopyrightInformation>Copyright © 2017 Australasian College for Infection Prevention and Control. Published by Elsevier B.V. All rights reserved.</CopyrightInformation>
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<Keyword MajorTopicYN="Y">Health communication</Keyword>
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<Keyword MajorTopicYN="Y">Social media</Keyword>
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<name sortKey="Fung, Isaac Chun Hai" sort="Fung, Isaac Chun Hai" uniqKey="Fung I" first="Isaac Chun-Hai" last="Fung">Isaac Chun-Hai Fung</name>
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<name sortKey="Liu, Zhaochong" sort="Liu, Zhaochong" uniqKey="Liu Z" first="Zhaochong" last="Liu">Zhaochong Liu</name>
<name sortKey="Tse, Zion Tsz Ho" sort="Tse, Zion Tsz Ho" uniqKey="Tse Z" first="Zion Tsz Ho" last="Tse">Zion Tsz Ho Tse</name>
<name sortKey="Yin, Jingjing" sort="Yin, Jingjing" uniqKey="Yin J" first="Jingjing" last="Yin">Jingjing Yin</name>
</country>
<country name="Australie">
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<name sortKey="Zeng, Jing" sort="Zeng, Jing" uniqKey="Zeng J" first="Jing" last="Zeng">Jing Zeng</name>
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<country name="Hong Kong">
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<name sortKey="Chan, Chung Hong" sort="Chan, Chung Hong" uniqKey="Chan C" first="Chung-Hong" last="Chan">Chung-Hong Chan</name>
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<name sortKey="Fu, King Wa" sort="Fu, King Wa" uniqKey="Fu K" first="King-Wa" last="Fu">King-Wa Fu</name>
<name sortKey="Liang, Hai" sort="Liang, Hai" uniqKey="Liang H" first="Hai" last="Liang">Hai Liang</name>
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