MersV1, PubMed, Corpus, bibRecord, 000A35

Classifying and Summarizing Information from Microblogs During Epidemics.

Identifieur interne : 000A35 ( PubMed/Corpus ); précédent : 000A34; suivant : 000A36

Classifying and Summarizing Information from Microblogs During Epidemics.

Auteurs : Koustav Rudra ; Ashish Sharma ; Niloy Ganguly ; Muhammad Imran

Source :

Information systems frontiers : a journal of research and innovation [ 1387-3326 ] ; 2018.

RBID : pubmed:32214879

Abstract

During a new disease outbreak, frustration and uncertainties among affected and vulnerable population increase. Affected communities look for known symptoms, prevention measures, and treatment strategies. On the other hand, health organizations try to get situational updates to assess the severity of the outbreak, known affected cases, and other details. Recent emergence of social media platforms such as Twitter provide convenient ways and fast access to disseminate and consume information to/from a wider audience. Research studies have shown potential of this online information to address information needs of concerned authorities during outbreaks, epidemics, and pandemics. In this work, we target three types of end-users (i) vulnerable population-people who are not yet affected and are looking for prevention related information (ii) affected population-people who are affected and looking for treatment related information, and (iii) health organizations-like WHO, who are interested in gaining situational awareness to make timely decisions. We use Twitter data from two recent outbreaks (Ebola and MERS) to build an automatic classification approach useful to categorize tweets into different disease related categories. Moreover, the classified messages are used to generate different kinds of summaries useful for affected and vulnerable communities as well as health organizations. Results obtained from extensive experimentation show the effectiveness of the proposed approach.

DOI: 10.1007/s10796-018-9844-9
PubMed: 32214879

Links to Exploration step

pubmed:32214879

Le document en format XML

<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Classifying and Summarizing Information from Microblogs During Epidemics.</title>
<author><name sortKey="Rudra, Koustav" sort="Rudra, Koustav" uniqKey="Rudra K" first="Koustav" last="Rudra">Koustav Rudra</name>
<affiliation><nlm:affiliation>1IIT Kharagpur, Kharagpur, India.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Sharma, Ashish" sort="Sharma, Ashish" uniqKey="Sharma A" first="Ashish" last="Sharma">Ashish Sharma</name>
<affiliation><nlm:affiliation>1IIT Kharagpur, Kharagpur, India.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Ganguly, Niloy" sort="Ganguly, Niloy" uniqKey="Ganguly N" first="Niloy" last="Ganguly">Niloy Ganguly</name>
<affiliation><nlm:affiliation>1IIT Kharagpur, Kharagpur, India.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Imran, Muhammad" sort="Imran, Muhammad" uniqKey="Imran M" first="Muhammad" last="Imran">Muhammad Imran</name>
<affiliation><nlm:affiliation>2Qatar Computing Research Institute, HBKU, Doha, Qatar.</nlm:affiliation>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PubMed</idno>
<date when="2018">2018</date>
<idno type="RBID">pubmed:32214879</idno>
<idno type="pmid">32214879</idno>
<idno type="doi">10.1007/s10796-018-9844-9</idno>
<idno type="wicri:Area/PubMed/Corpus">000A35</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000A35</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en">Classifying and Summarizing Information from Microblogs During Epidemics.</title>
<author><name sortKey="Rudra, Koustav" sort="Rudra, Koustav" uniqKey="Rudra K" first="Koustav" last="Rudra">Koustav Rudra</name>
<affiliation><nlm:affiliation>1IIT Kharagpur, Kharagpur, India.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Sharma, Ashish" sort="Sharma, Ashish" uniqKey="Sharma A" first="Ashish" last="Sharma">Ashish Sharma</name>
<affiliation><nlm:affiliation>1IIT Kharagpur, Kharagpur, India.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Ganguly, Niloy" sort="Ganguly, Niloy" uniqKey="Ganguly N" first="Niloy" last="Ganguly">Niloy Ganguly</name>
<affiliation><nlm:affiliation>1IIT Kharagpur, Kharagpur, India.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Imran, Muhammad" sort="Imran, Muhammad" uniqKey="Imran M" first="Muhammad" last="Imran">Muhammad Imran</name>
<affiliation><nlm:affiliation>2Qatar Computing Research Institute, HBKU, Doha, Qatar.</nlm:affiliation>
</affiliation>
</author>
</analytic>
<series><title level="j">Information systems frontiers : a journal of research and innovation</title>
<idno type="ISSN">1387-3326</idno>
<imprint><date when="2018" type="published">2018</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">During a new disease outbreak, frustration and uncertainties among affected and vulnerable population increase. Affected communities look for known symptoms, prevention measures, and treatment strategies. On the other hand, health organizations try to get situational updates to assess the severity of the outbreak, known affected cases, and other details. Recent emergence of social media platforms such as Twitter provide convenient ways and fast access to disseminate and consume information to/from a wider audience. Research studies have shown potential of this online information to address information needs of concerned authorities during outbreaks, epidemics, and pandemics. In this work, we target three types of end-users (i) vulnerable population-people who are not yet affected and are looking for prevention related information (ii) affected population-people who are affected and looking for treatment related information, and (iii) health organizations-like WHO, who are interested in gaining situational awareness to make timely decisions. We use Twitter data from two recent outbreaks (Ebola and MERS) to build an automatic classification approach useful to categorize tweets into different disease related categories. Moreover, the classified messages are used to generate different kinds of summaries useful for affected and vulnerable communities as well as health organizations. Results obtained from extensive experimentation show the effectiveness of the proposed approach.</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32214879</PMID>
<DateRevised><Year>2020</Year>
<Month>03</Month>
<Day>31</Day>
</DateRevised>
<Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">1387-3326</ISSN>
<JournalIssue CitedMedium="Print"><Volume>20</Volume>
<Issue>5</Issue>
<PubDate><Year>2018</Year>
</PubDate>
</JournalIssue>
<Title>Information systems frontiers : a journal of research and innovation</Title>
<ISOAbbreviation>Inf Syst Front</ISOAbbreviation>
</Journal>
<ArticleTitle>Classifying and Summarizing Information from Microblogs During Epidemics.</ArticleTitle>
<Pagination><MedlinePgn>933-948</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1007/s10796-018-9844-9</ELocationID>
<Abstract><AbstractText>During a new disease outbreak, frustration and uncertainties among affected and vulnerable population increase. Affected communities look for known symptoms, prevention measures, and treatment strategies. On the other hand, health organizations try to get situational updates to assess the severity of the outbreak, known affected cases, and other details. Recent emergence of social media platforms such as Twitter provide convenient ways and fast access to disseminate and consume information to/from a wider audience. Research studies have shown potential of this online information to address information needs of concerned authorities during outbreaks, epidemics, and pandemics. In this work, we target three types of end-users (i) vulnerable population-people who are not yet affected and are looking for prevention related information (ii) affected population-people who are affected and looking for treatment related information, and (iii) health organizations-like WHO, who are interested in gaining situational awareness to make timely decisions. We use Twitter data from two recent outbreaks (Ebola and MERS) to build an automatic classification approach useful to categorize tweets into different disease related categories. Moreover, the classified messages are used to generate different kinds of summaries useful for affected and vulnerable communities as well as health organizations. Results obtained from extensive experimentation show the effectiveness of the proposed approach.</AbstractText>
<CopyrightInformation>© Springer Science+Business Media, LLC, part of Springer Nature 2018.</CopyrightInformation>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Rudra</LastName>
<ForeName>Koustav</ForeName>
<Initials>K</Initials>
<Identifier Source="ORCID">0000-0002-2486-7608</Identifier>
<AffiliationInfo><Affiliation>1IIT Kharagpur, Kharagpur, India.</Affiliation>
<Identifier Source="ISNI">0000 0001 0153 2859</Identifier>
<Identifier Source="GRID">grid.429017.9</Identifier>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Sharma</LastName>
<ForeName>Ashish</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>1IIT Kharagpur, Kharagpur, India.</Affiliation>
<Identifier Source="ISNI">0000 0001 0153 2859</Identifier>
<Identifier Source="GRID">grid.429017.9</Identifier>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Ganguly</LastName>
<ForeName>Niloy</ForeName>
<Initials>N</Initials>
<AffiliationInfo><Affiliation>1IIT Kharagpur, Kharagpur, India.</Affiliation>
<Identifier Source="ISNI">0000 0001 0153 2859</Identifier>
<Identifier Source="GRID">grid.429017.9</Identifier>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Imran</LastName>
<ForeName>Muhammad</ForeName>
<Initials>M</Initials>
<AffiliationInfo><Affiliation>2Qatar Computing Research Institute, HBKU, Doha, Qatar.</Affiliation>
<Identifier Source="ISNI">0000 0004 1789 3191</Identifier>
<Identifier Source="GRID">grid.452146.0</Identifier>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2018</Year>
<Month>03</Month>
<Day>20</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>United States</Country>
<MedlineTA>Inf Syst Front</MedlineTA>
<NlmUniqueID>101685853</NlmUniqueID>
<ISSNLinking>1387-3326</ISSNLinking>
</MedlineJournalInfo>
<KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Classification</Keyword>
<Keyword MajorTopicYN="N">Epidemic</Keyword>
<Keyword MajorTopicYN="N">Health crisis</Keyword>
<Keyword MajorTopicYN="N">Summarization</Keyword>
<Keyword MajorTopicYN="N">Twitter</Keyword>
</KeywordList>
<CoiStatement>Competing interestsThe authors don’t have any competing interests in this paper.</CoiStatement>
</MedlineCitation>
<PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year>
<Month>3</Month>
<Day>28</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed"><Year>2018</Year>
<Month>1</Month>
<Day>1</Day>
<Hour>0</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline"><Year>2018</Year>
<Month>1</Month>
<Day>1</Day>
<Hour>0</Hour>
<Minute>1</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>ppublish</PublicationStatus>
<ArticleIdList><ArticleId IdType="pubmed">32214879</ArticleId>
<ArticleId IdType="doi">10.1007/s10796-018-9844-9</ArticleId>
<ArticleId IdType="pii">9844</ArticleId>
<ArticleId IdType="pmc">PMC7087635</ArticleId>
</ArticleIdList>
<pmc-dir>pmcsd</pmc-dir>
        <ReferenceList><Reference><Citation>Proc AMIA Symp. 2001;:662-6</Citation>
<ArticleIdList><ArticleId IdType="pubmed">11825268</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>Proc AMIA Symp. 2001;:17-21</Citation>
<ArticleIdList><ArticleId IdType="pubmed">11825149</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>J Am Med Inform Assoc. 2011 Sep-Oct;18(5):552-6</Citation>
<ArticleIdList><ArticleId IdType="pubmed">21685143</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>Support Care Cancer. 2010 Sep;18(9):1123-36</Citation>
<ArticleIdList><ArticleId IdType="pubmed">20336326</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>Nucleic Acids Res. 2004 Jan 1;32(Database issue):D267-70</Citation>
<ArticleIdList><ArticleId IdType="pubmed">14681409</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>J Am Med Inform Assoc. 2010 Sep-Oct;17(5):507-13</Citation>
<ArticleIdList><ArticleId IdType="pubmed">20819853</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>J Med Syst. 2016 Nov;40(11):236</Citation>
<ArticleIdList><ArticleId IdType="pubmed">27663246</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>J Am Med Inform Assoc. 2011 Sep-Oct;18(5):568-73</Citation>
<ArticleIdList><ArticleId IdType="pubmed">21724741</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>CSCW Conf Comput Support Coop Work. 2014 Feb;2014:615-625</Citation>
<ArticleIdList><ArticleId IdType="pubmed">28492067</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>J Med Internet Res. 2015 Aug 31;17(8):e212</Citation>
<ArticleIdList><ArticleId IdType="pubmed">26323337</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>Proc AMIA Symp. 2001;:254-8</Citation>
<ArticleIdList><ArticleId IdType="pubmed">11825190</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>J Am Med Inform Assoc. 2004 Sep-Oct;11(5):392-402</Citation>
<ArticleIdList><ArticleId IdType="pubmed">15187068</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>J Am Med Inform Assoc. 1999 Jan-Feb;6(1):76-87</Citation>
<ArticleIdList><ArticleId IdType="pubmed">9925230</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>Radiology. 2002 Jul;224(1):157-63</Citation>
<ArticleIdList><ArticleId IdType="pubmed">12091676</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>Am J Infect Control. 2010 Apr;38(3):182-8</Citation>
<ArticleIdList><ArticleId IdType="pubmed">20347636</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>Proc ACM Int Conf Inf Knowl Manag. 2016 Oct;2016:297-306</Citation>
<ArticleIdList><ArticleId IdType="pubmed">28758046</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
</PubmedData>
</pubmed>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Sante/explor/MersV1/Data/PubMed/Corpus

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000A35 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/biblio.hfd -nk 000A35 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Sante
   |area=    MersV1
   |flux=    PubMed
   |étape=   Corpus
   |type=    RBID
   |clé=     pubmed:32214879
   |texte=   Classifying and Summarizing Information from Microblogs During Epidemics.
}}

Pour générer des pages wiki

HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/RBID.i   -Sk "pubmed:32214879" \
       | HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Corpus/biblio.hfd   \
       | NlmPubMed2Wicri -a MersV1

This area was generated with Dilib version V0.6.33.
Data generation: Mon Apr 20 23:26:43 2020. Site generation: Sat Mar 27 09:06:09 2021

	Serveur d'exploration MERS
	Attention, ce site est en cours de développement ! Attention, site généré par des moyens informatiques à partir de corpus bruts. Les informations ne sont donc pas validées.

Serveur d'exploration MERS

Classifying and Summarizing Information from Microblogs During Epidemics.

Classifying and Summarizing Information from Microblogs During Epidemics.

Source :

Abstract

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki