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 : 000F17 ( Pmc/Curation ); précédent : 000F16; suivant : 000F18

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

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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<pmc article-type="research-article">
<pmc-dir>properties open_access</pmc-dir>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">Methods</journal-id>
<journal-id journal-id-type="iso-abbrev">Methods</journal-id>
<journal-title-group>
<journal-title>Methods (San Diego, Calif.)</journal-title>
</journal-title-group>
<issn pub-type="ppub">1046-2023</issn>
<issn pub-type="epub">1095-9130</issn>
<publisher>
<publisher-name>Elsevier Inc.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">28813689</article-id>
<article-id pub-id-type="pmc">7129462</article-id>
<article-id pub-id-type="publisher-id">S1046-2023(17)30019-1</article-id>
<article-id pub-id-type="doi">10.1016/j.ymeth.2017.07.027</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Article</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" id="au005">
<name>
<surname>Choi</surname>
<given-names>Sungwoon</given-names>
</name>
<email>nebulach23@gmail.com</email>
<xref rid="af005" ref-type="aff">a</xref>
<xref rid="af010" ref-type="aff">b</xref>
</contrib>
<contrib contrib-type="author" id="au010">
<name>
<surname>Lee</surname>
<given-names>Jangho</given-names>
</name>
<email>ubuntu@snu.ac.kr</email>
<xref rid="af005" ref-type="aff">a</xref>
</contrib>
<contrib contrib-type="author" id="au015">
<name>
<surname>Kang</surname>
<given-names>Min-Gyu</given-names>
</name>
<email>irreversibly@gmail.com</email>
<xref rid="af015" ref-type="aff">c</xref>
</contrib>
<contrib contrib-type="author" id="au020">
<name>
<surname>Min</surname>
<given-names>Hyeyoung</given-names>
</name>
<email>hymin@cau.ac.kr</email>
<xref rid="af020" ref-type="aff">d</xref>
</contrib>
<contrib contrib-type="author" id="au025">
<name>
<surname>Chang</surname>
<given-names>Yoon-Seok</given-names>
</name>
<email>addchang@snu.ac.kr</email>
<xref rid="af025" ref-type="aff">e</xref>
<xref rid="cor1" ref-type="corresp"></xref>
</contrib>
<contrib contrib-type="author" id="au030">
<name>
<surname>Yoon</surname>
<given-names>Sungroh</given-names>
</name>
<email>sryoon@snu.ac.kr</email>
<email>http://data.snu.ac.kr</email>
<xref rid="af005" ref-type="aff">a</xref>
<xref rid="af030" ref-type="aff">f</xref>
<xref rid="cor1" ref-type="corresp"></xref>
</contrib>
</contrib-group>
<aff id="af005">
<label>a</label>
Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea</aff>
<aff id="af010">
<label>b</label>
IT Convergence, Korea University, Seoul 02841, Republic of Korea</aff>
<aff id="af015">
<label>c</label>
Internal Medicine, Chungbuk National University Hospital, Cheongju 28644, Republic of Korea</aff>
<aff id="af020">
<label>d</label>
RNA Biopharmacy Laboratory, College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea</aff>
<aff id="af025">
<label>e</label>
Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Gyeonggi-do 13620, Republic of Korea</aff>
<aff id="af030">
<label>f</label>
Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA</aff>
<author-notes>
<corresp id="cor1">
<label></label>
Corresponding authors at: Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Gyeonggi-do 13620, Republic of Korea (Y-S Chang), Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea (S. Yoon).
<email>addchang@snu.ac.kr</email>
<email>sryoon@snu.ac.kr</email>
<email>http://data.snu.ac.kr</email>
</corresp>
</author-notes>
<pub-date pub-type="pmc-release">
<day>13</day>
<month>8</month>
<year>2017</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="ppub">
<day>1</day>
<month>10</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="epub">
<day>13</day>
<month>8</month>
<year>2017</year>
</pub-date>
<volume>129</volume>
<fpage>50</fpage>
<lpage>59</lpage>
<history>
<date date-type="received">
<day>15</day>
<month>1</month>
<year>2017</year>
</date>
<date date-type="rev-recd">
<day>29</day>
<month>7</month>
<year>2017</year>
</date>
<date date-type="accepted">
<day>31</day>
<month>7</month>
<year>2017</year>
</date>
</history>
<permissions>
<copyright-statement>© 2017 Elsevier Inc. All rights reserved.</copyright-statement>
<copyright-year>2017</copyright-year>
<copyright-holder>Elsevier Inc.</copyright-holder>
<license>
<license-p>Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.</license-p>
</license>
</permissions>
<abstract abstract-type="author-highlights" id="ab005">
<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>
</abstract>
<abstract id="ab010">
<p>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.</p>
</abstract>
<kwd-group id="kg005">
<title>Keywords</title>
<kwd>Machine learning</kwd>
<kwd>Middle East respiratory syndrome (MERS)</kwd>
<kwd>Natural language processing</kwd>
<kwd>Sentiment analysis</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
</record>

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