Serveur d'exploration MERS

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Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks

Identifieur interne : 001B38 ( Ncbi/Merge ); précédent : 001B37; suivant : 001B39

Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks

Auteurs : Sungwoon Choi [Corée du Sud] ; Jangho Lee [Corée du Sud] ; Min-Gyu Kang [Corée du Sud] ; Hyeyoung Min [Corée du Sud] ; Yoon-Seok Chang [Corée du Sud] ; Sungroh Yoon [Corée du Sud, États-Unis]

Source :

RBID : PMC:7129462

Descripteurs français

English descriptors

Abstract

Highlights

The emotional public responses to a nation-wide outbreak of Middle East respiratory syndrome (MERS) in Korea was analyzed.

Massive media outlet data was collected during the outbreak.

An intriguing loop of information transfers between the media and the public was discovered.

This method would be helpful for alleviating the unnecessary fear and overreaction of the public regarding infectious diseases.


Url:
DOI: 10.1016/j.ymeth.2017.07.027
PubMed: 28813689
PubMed Central: 7129462

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


Links to Exploration step

PMC:7129462

Le document en format XML

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<p id="p0265">The emotional public responses to a nation-wide outbreak of Middle East respiratory syndrome (MERS) in Korea was analyzed.</p>
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<p id="p0270">Massive media outlet data was collected during the outbreak.</p>
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<p id="p0275">An intriguing loop of information transfers between the media and the public was discovered.</p>
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<wicri:regionArea>Electrical and Computer Engineering, Seoul National University, Seoul 08826</wicri:regionArea>
<placeName>
<settlement type="city">Séoul</settlement>
<region type="capital">Région capitale de Séoul</region>
</placeName>
<orgName type="university">Université nationale de Séoul</orgName>
<placeName>
<settlement type="city">Séoul</settlement>
</placeName>
</affiliation>
<affiliation wicri:level="2">
<nlm:aff id="af030">Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305</wicri:regionArea>
<placeName>
<region type="state">Californie</region>
</placeName>
</affiliation>
</author>
</analytic>
<series>
<title level="j">Methods (San Diego, Calif.)</title>
<idno type="ISSN">1046-2023</idno>
<idno type="eISSN">1095-9130</idno>
<imprint>
<date when="2017">2017</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc>
<textClass></textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">
<title>Highlights</title>
<p>
<list list-type="simple" id="l0005">
<list-item id="u0005">
<label></label>
<p id="p0265">The emotional public responses to a nation-wide outbreak of Middle East respiratory syndrome (MERS) in Korea was analyzed.</p>
</list-item>
<list-item id="u0010">
<label></label>
<p id="p0270">Massive media outlet data was collected during the outbreak.</p>
</list-item>
<list-item id="u0015">
<label></label>
<p id="p0275">An intriguing loop of information transfers between the media and the public was discovered.</p>
</list-item>
<list-item id="u0020">
<label></label>
<p id="p0280">This method would be helpful for alleviating the unnecessary fear and overreaction of the public regarding infectious diseases.</p>
</list-item>
</list>
</p>
</div>
</front>
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</div1>
</back>
</TEI>
</pmc>
<pubmed>
<TEI>
<teiHeader>
<fileDesc>
<titleStmt>
<title xml:lang="en">Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks.</title>
<author>
<name sortKey="Choi, Sungwoon" sort="Choi, Sungwoon" uniqKey="Choi S" first="Sungwoon" last="Choi">Sungwoon Choi</name>
<affiliation wicri:level="4">
<nlm:affiliation>Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea; IT Convergence, Korea University, Seoul 02841, Republic of Korea. Electronic address: nebulach23@gmail.com.</nlm:affiliation>
<country xml:lang="fr">Corée du Sud</country>
<wicri:regionArea>Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea; IT Convergence, Korea University, Seoul 02841</wicri:regionArea>
<placeName>
<settlement type="city">Séoul</settlement>
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<name sortKey="Kang, Min Gyu" sort="Kang, Min Gyu" uniqKey="Kang M" first="Min-Gyu" last="Kang">Min-Gyu Kang</name>
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<name sortKey="Min, Hyeyoung" sort="Min, Hyeyoung" uniqKey="Min H" first="Hyeyoung" last="Min">Hyeyoung Min</name>
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<title xml:lang="en">Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks.</title>
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<name sortKey="Choi, Sungwoon" sort="Choi, Sungwoon" uniqKey="Choi S" first="Sungwoon" last="Choi">Sungwoon Choi</name>
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<nlm:affiliation>Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea; IT Convergence, Korea University, Seoul 02841, Republic of Korea. Electronic address: nebulach23@gmail.com.</nlm:affiliation>
<country xml:lang="fr">Corée du Sud</country>
<wicri:regionArea>Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea; IT Convergence, Korea University, Seoul 02841</wicri:regionArea>
<placeName>
<settlement type="city">Séoul</settlement>
<region type="capital">Région capitale de Séoul</region>
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<orgName type="university">Université nationale de Séoul</orgName>
<placeName>
<settlement type="city">Séoul</settlement>
</placeName>
</affiliation>
</author>
<author>
<name sortKey="Lee, Jangho" sort="Lee, Jangho" uniqKey="Lee J" first="Jangho" last="Lee">Jangho Lee</name>
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<nlm:affiliation>Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea. Electronic address: ubuntu@snu.ac.kr.</nlm:affiliation>
<country xml:lang="fr">Corée du Sud</country>
<wicri:regionArea>Electrical and Computer Engineering, Seoul National University, Seoul 08826</wicri:regionArea>
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<settlement type="city">Séoul</settlement>
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<placeName>
<settlement type="city">Séoul</settlement>
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</affiliation>
</author>
<author>
<name sortKey="Kang, Min Gyu" sort="Kang, Min Gyu" uniqKey="Kang M" first="Min-Gyu" last="Kang">Min-Gyu Kang</name>
<affiliation wicri:level="1">
<nlm:affiliation>Internal Medicine, Chungbuk National University Hospital, Cheongju 28644, Republic of Korea. Electronic address: irreversibly@gmail.com.</nlm:affiliation>
<country xml:lang="fr">Corée du Sud</country>
<wicri:regionArea>Internal Medicine, Chungbuk National University Hospital, Cheongju 28644</wicri:regionArea>
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<name sortKey="Min, Hyeyoung" sort="Min, Hyeyoung" uniqKey="Min H" first="Hyeyoung" last="Min">Hyeyoung Min</name>
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<nlm:affiliation>RNA Biopharmacy Laboratory, College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea. Electronic address: hymin@cau.ac.kr.</nlm:affiliation>
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<wicri:regionArea>RNA Biopharmacy Laboratory, College of Pharmacy, Chung-Ang University, Seoul 06974</wicri:regionArea>
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<settlement type="city">Séoul</settlement>
<region type="capital">Région capitale de Séoul</region>
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<name sortKey="Chang, Yoon Seok" sort="Chang, Yoon Seok" uniqKey="Chang Y" first="Yoon-Seok" last="Chang">Yoon-Seok Chang</name>
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<nlm:affiliation>Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Gyeonggi-do 13620, Republic of Korea. Electronic address: addchang@snu.ac.kr.</nlm:affiliation>
<country xml:lang="fr">Corée du Sud</country>
<wicri:regionArea>Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Gyeonggi-do 13620</wicri:regionArea>
<wicri:noRegion>Gyeonggi-do 13620</wicri:noRegion>
</affiliation>
</author>
<author>
<name sortKey="Yoon, Sungroh" sort="Yoon, Sungroh" uniqKey="Yoon S" first="Sungroh" last="Yoon">Sungroh Yoon</name>
<affiliation wicri:level="4">
<nlm:affiliation>Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea; Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA. Electronic address: sryoon@snu.ac.kr.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea; Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305</wicri:regionArea>
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<region type="state">Californie</region>
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<series>
<title level="j">Methods (San Diego, Calif.)</title>
<idno type="eISSN">1095-9130</idno>
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<date when="2017" type="published">2017</date>
</imprint>
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<term>Coronavirus Infections (epidemiology)</term>
<term>Coronavirus Infections (virology)</term>
<term>Disease Outbreaks</term>
<term>Humans</term>
<term>Machine Learning</term>
<term>Mass Media</term>
<term>Middle East Respiratory Syndrome Coronavirus (pathogenicity)</term>
<term>Republic of Korea</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr">
<term>Apprentissage machine</term>
<term>Coronavirus du syndrome respiratoire du Moyen-Orient (pathogénicité)</term>
<term>Flambées de maladies</term>
<term>Humains</term>
<term>Infections à coronavirus (virologie)</term>
<term>Infections à coronavirus (épidémiologie)</term>
<term>Mass-médias</term>
<term>République de Corée</term>
</keywords>
<keywords scheme="MESH" qualifier="epidemiology" xml:lang="en">
<term>Coronavirus Infections</term>
</keywords>
<keywords scheme="MESH" qualifier="pathogenicity" xml:lang="en">
<term>Middle East Respiratory Syndrome Coronavirus</term>
</keywords>
<keywords scheme="MESH" qualifier="pathogénicité" xml:lang="fr">
<term>Coronavirus du syndrome respiratoire du Moyen-Orient</term>
</keywords>
<keywords scheme="MESH" qualifier="virologie" xml:lang="fr">
<term>Infections à coronavirus</term>
</keywords>
<keywords scheme="MESH" qualifier="virology" xml:lang="en">
<term>Coronavirus Infections</term>
</keywords>
<keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr">
<term>Infections à coronavirus</term>
</keywords>
<keywords scheme="MESH" xml:lang="en">
<term>Disease Outbreaks</term>
<term>Humans</term>
<term>Machine Learning</term>
<term>Mass Media</term>
<term>Republic of Korea</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr">
<term>Apprentissage machine</term>
<term>Flambées de maladies</term>
<term>Humains</term>
<term>Mass-médias</term>
<term>République de Corée</term>
</keywords>
</textClass>
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<front>
<div type="abstract" xml:lang="en">From May to July 2015, there was a nation-wide outbreak of Middle East respiratory syndrome (MERS) in Korea. MERS is caused by MERS-CoV, an enveloped, positive-sense, single-stranded RNA virus belonging to the family Coronaviridae. Despite expert opinions that the danger of MERS might be exaggerated, there was an overreaction by the public according to the Korean mass media, which led to a noticeable reduction in social and economic activities during the outbreak. To explain this phenomenon, we presumed that machine learning-based analysis of media outlets would be helpful and collected a number of Korean mass media articles and short-text comments produced during the 10-week outbreak. To process and analyze the collected data (over 86 million words in total) effectively, we created a methodology composed of machine-learning and information-theoretic approaches. Our proposal included techniques for extracting emotions from emoticons and Internet slang, which allowed us to significantly (approximately 73%) increase the number of emotion-bearing texts needed for robust sentiment analysis of social media. As a result, we discovered a plausible explanation for the public overreaction to MERS in terms of the interplay between the disease, mass media, and public emotions.</div>
</front>
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