Application of Topic Modeling to Tweets as the Foundation for Health Disparity Research for COVID-19.
Identifieur interne : 000796 ( Main/Exploration ); précédent : 000795; suivant : 000797Application of Topic Modeling to Tweets as the Foundation for Health Disparity Research for COVID-19.
Auteurs : Michelle Odlum [États-Unis] ; Hwayoung Cho [États-Unis] ; Peter Broadwell [États-Unis] ; Nicole Davis [États-Unis] ; Maria Patrao [États-Unis] ; Deborah Schauer ; Michael E. Bales [États-Unis] ; Carmela Alcantara [États-Unis] ; Sunmoo Yoon [États-Unis]Source :
- Studies in health technology and informatics [ 1879-8365 ] ; 2020.
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Abstract
We randomly extracted publicly available Tweets mentioning COVID-19 related terms (n=2,558,474 Tweets) from Tweet corpora collected daily using an API from Jan 21st to May 3rd, 2020. We applied a clustering algorithm to publicly available Tweets authored by African Americans (n=1,763) to detect topics and sentiment applying natural language processing (NLP). We visualized fifteen topics (four themes) using network diagrams (Newman modularity 0.74). Compared to the COVID-19 related Tweets authored by others, positive sentiments, cohesively encouraging online discussions (e.g., Black strong 27.1%, growing up Blacks 22.8%, support Black business 17.0%, how to build resilience 7.8%), and COVID-19 prevention behaviors (e.g., masks 4.7%, encouraging social distancing 9.4%) were uniquely observed in African American Twitter communities. Application of topic modeling techniques to streaming social media Twitter provides the foundation for research team insights regarding information and future virtual based intervention and social media based health disparity research for COVID-19.
DOI: 10.3233/SHTI200484
PubMed: 32604591
PubMed Central: PMC7728402
Affiliations:
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Bales</name><affiliation wicri:level="1"><nlm:affiliation>Weill Cornell Medicine, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Weill Cornell Medicine</wicri:regionArea></affiliation></author><author><name sortKey="Alcantara, Carmela" sort="Alcantara, Carmela" uniqKey="Alcantara C" first="Carmela" last="Alcantara">Carmela Alcantara</name><affiliation wicri:level="4"><nlm:affiliation>School of Social Work, Columbia University, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of Social Work, Columbia University</wicri:regionArea><placeName><settlement type="city">New York</settlement><region type="state">État de New York</region></placeName><orgName type="university">Université Columbia</orgName></affiliation></author><author><name sortKey="Yoon, Sunmoo" sort="Yoon, Sunmoo" uniqKey="Yoon S" first="Sunmoo" last="Yoon">Sunmoo Yoon</name><affiliation wicri:level="1"><nlm:affiliation>Columbia University Irving Medical Center, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Columbia University Irving Medical Center</wicri:regionArea></affiliation></author></analytic><series><title level="j">Studies in health technology and informatics</title><idno type="eISSN">1879-8365</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Betacoronavirus (MeSH)</term><term>COVID-19 (MeSH)</term><term>Coronavirus Infections (MeSH)</term><term>Humans (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (MeSH)</term><term>SARS-CoV-2 (MeSH)</term><term>Social Media (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Betacoronavirus (MeSH)</term><term>Humains (MeSH)</term><term>Infections à coronavirus (MeSH)</term><term>Médias sociaux (MeSH)</term><term>Pandémies (MeSH)</term><term>Pneumopathie virale (MeSH)</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Betacoronavirus</term><term>COVID-19</term><term>Coronavirus Infections</term><term>Humans</term><term>Pandemics</term><term>Pneumonia, Viral</term><term>SARS-CoV-2</term><term>Social Media</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Betacoronavirus</term><term>Humains</term><term>Infections à coronavirus</term><term>Médias sociaux</term><term>Pandémies</term><term>Pneumopathie virale</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We randomly extracted publicly available Tweets mentioning COVID-19 related terms (n=2,558,474 Tweets) from Tweet corpora collected daily using an API from Jan 21st to May 3rd, 2020. We applied a clustering algorithm to publicly available Tweets authored by African Americans (n=1,763) to detect topics and sentiment applying natural language processing (NLP). We visualized fifteen topics (four themes) using network diagrams (Newman modularity 0.74). Compared to the COVID-19 related Tweets authored by others, positive sentiments, cohesively encouraging online discussions (e.g., Black strong 27.1%, growing up Blacks 22.8%, support Black business 17.0%, how to build resilience 7.8%), and COVID-19 prevention behaviors (e.g., masks 4.7%, encouraging social distancing 9.4%) were uniquely observed in African American Twitter communities. Application of topic modeling techniques to streaming social media Twitter provides the foundation for research team insights regarding information and future virtual based intervention and social media based health disparity research for COVID-19.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">32604591</PMID><DateCompleted><Year>2020</Year><Month>07</Month><Day>06</Day></DateCompleted><DateRevised><Year>2020</Year><Month>12</Month><Day>26</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1879-8365</ISSN><JournalIssue CitedMedium="Internet"><Volume>272</Volume><PubDate><Year>2020</Year><Month>Jun</Month><Day>26</Day></PubDate></JournalIssue><Title>Studies in health technology and informatics</Title><ISOAbbreviation>Stud Health Technol Inform</ISOAbbreviation></Journal><ArticleTitle>Application of Topic Modeling to Tweets as the Foundation for Health Disparity Research for COVID-19.</ArticleTitle><Pagination><MedlinePgn>24-27</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3233/SHTI200484</ELocationID><Abstract><AbstractText>We randomly extracted publicly available Tweets mentioning COVID-19 related terms (n=2,558,474 Tweets) from Tweet corpora collected daily using an API from Jan 21st to May 3rd, 2020. We applied a clustering algorithm to publicly available Tweets authored by African Americans (n=1,763) to detect topics and sentiment applying natural language processing (NLP). We visualized fifteen topics (four themes) using network diagrams (Newman modularity 0.74). Compared to the COVID-19 related Tweets authored by others, positive sentiments, cohesively encouraging online discussions (e.g., Black strong 27.1%, growing up Blacks 22.8%, support Black business 17.0%, how to build resilience 7.8%), and COVID-19 prevention behaviors (e.g., masks 4.7%, encouraging social distancing 9.4%) were uniquely observed in African American Twitter communities. Application of topic modeling techniques to streaming social media Twitter provides the foundation for research team insights regarding information and future virtual based intervention and social media based health disparity research for COVID-19.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Odlum</LastName><ForeName>Michelle</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>School of Nursing, Columbia University, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cho</LastName><ForeName>Hwayoung</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>College of Nursing, University of Florida, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Broadwell</LastName><ForeName>Peter</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Stanford University, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Davis</LastName><ForeName>Nicole</ForeName><Initials>N</Initials><AffiliationInfo><Affiliation>School of Nursing, Clemson University, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Patrao</LastName><ForeName>Maria</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>New York Presbyterian Hospital, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Schauer</LastName><ForeName>Deborah</ForeName><Initials>D</Initials></Author><Author ValidYN="Y"><LastName>Bales</LastName><ForeName>Michael E</ForeName><Initials>ME</Initials><AffiliationInfo><Affiliation>Weill Cornell Medicine, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Alcantara</LastName><ForeName>Carmela</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>School of Social Work, Columbia University, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yoon</LastName><ForeName>Sunmoo</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Columbia University Irving Medical Center, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>R01 AG060929</GrantID><Acronym>AG</Acronym><Agency>NIA NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Stud Health Technol Inform</MedlineTA><NlmUniqueID>9214582</NlmUniqueID><ISSNLinking>0926-9630</ISSNLinking></MedlineJournalInfo><CitationSubset>T</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="Y">Betacoronavirus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="N">COVID-19</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="Y">Coronavirus Infections</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="Y">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="Y">Pneumonia, Viral</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086402" MajorTopicYN="N">SARS-CoV-2</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D061108" MajorTopicYN="N">Social Media</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">health disparities</Keyword><Keyword MajorTopicYN="N">pandemic</Keyword><Keyword MajorTopicYN="N">social media</Keyword><Keyword MajorTopicYN="N">virtual intervention</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate 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IdType="pubmed">32603243</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>État de New York</li></region><settlement><li>New York</li></settlement><orgName><li>Université Columbia</li></orgName></list><tree><noCountry><name sortKey="Schauer, Deborah" sort="Schauer, Deborah" uniqKey="Schauer D" first="Deborah" last="Schauer">Deborah Schauer</name></noCountry><country name="États-Unis"><region name="État de New York"><name sortKey="Odlum, Michelle" sort="Odlum, Michelle" uniqKey="Odlum M" first="Michelle" last="Odlum">Michelle Odlum</name></region><name sortKey="Alcantara, Carmela" sort="Alcantara, Carmela" uniqKey="Alcantara C" first="Carmela" last="Alcantara">Carmela Alcantara</name><name sortKey="Bales, Michael E" sort="Bales, Michael E" uniqKey="Bales M" first="Michael E" last="Bales">Michael E. Bales</name><name sortKey="Broadwell, Peter" sort="Broadwell, Peter" uniqKey="Broadwell P" first="Peter" last="Broadwell">Peter Broadwell</name><name sortKey="Cho, Hwayoung" sort="Cho, Hwayoung" uniqKey="Cho H" first="Hwayoung" last="Cho">Hwayoung Cho</name><name sortKey="Davis, Nicole" sort="Davis, Nicole" uniqKey="Davis N" first="Nicole" last="Davis">Nicole Davis</name><name sortKey="Patrao, Maria" sort="Patrao, Maria" uniqKey="Patrao M" first="Maria" last="Patrao">Maria Patrao</name><name sortKey="Yoon, Sunmoo" sort="Yoon, Sunmoo" uniqKey="Yoon S" first="Sunmoo" last="Yoon">Sunmoo Yoon</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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