Analyzing Spanish News Frames on Twitter during COVID-19-A Network Study of El País and El Mundo.
Identifieur interne : 001E87 ( Main/Corpus ); précédent : 001E86; suivant : 001E88Analyzing Spanish News Frames on Twitter during COVID-19-A Network Study of El País and El Mundo.
Auteurs : Jingyuan Yu ; Yanqin Lu ; Juan Mu Oz-JusticiaSource :
- International journal of environmental research and public health [ 1660-4601 ] ; 2020.
English descriptors
- KwdEn :
- Betacoronavirus (isolation & purification), COVID-19 (MeSH), Coronavirus Infections (epidemiology), Coronavirus Infections (virology), Humans (MeSH), Mass Media (MeSH), Pandemics (MeSH), Pneumonia, Viral (epidemiology), Pneumonia, Viral (virology), Public Health (MeSH), SARS-CoV-2 (MeSH), Social Media (MeSH), Spain (epidemiology).
- MESH :
- geographic , epidemiology : Spain.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- isolation & purification : Betacoronavirus.
- virology : Coronavirus Infections, Pneumonia, Viral.
- COVID-19, Humans, Mass Media, Pandemics, Public Health, SARS-CoV-2, Social Media.
Abstract
While COVID-19 is becoming one of the most severe public health crises in the twenty-first century, media coverage about this pandemic is getting more important than ever to make people informed. Drawing on data scraped from Twitter, this study aims to analyze and compare the news updates of two main Spanish newspapers El País and El Mundo during the pandemic. Throughout an automatic process of topic modeling and network analysis methods, this study identifies eight news frames for each newspaper's Twitter account. Furthermore, the whole pandemic development process is split into three periods-the pre-crisis period, the lockdown period and the recovery period. The networks of the computed frames are visualized by these three segments. This paper contributes to the understanding of how Spanish news media cover public health crises on social media platforms.
DOI: 10.3390/ijerph17155414
PubMed: 32731359
PubMed Central: PMC7432441
Links to Exploration step
pubmed:32731359Le document en format XML
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Drawing on data scraped from Twitter, this study aims to analyze and compare the news updates of two main Spanish newspapers <i>El País</i> and <i>El Mundo</i> during the pandemic. Throughout an automatic process of topic modeling and network analysis methods, this study identifies eight news frames for each newspaper's Twitter account. Furthermore, the whole pandemic development process is split into three periods-the pre-crisis period, the lockdown period and the recovery period. The networks of the computed frames are visualized by these three segments. This paper contributes to the understanding of how Spanish news media cover public health crises on social media platforms.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">32731359</PMID><DateCompleted><Year>2020</Year><Month>08</Month><Day>21</Day></DateCompleted><DateRevised><Year>2020</Year><Month>12</Month><Day>18</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1660-4601</ISSN><JournalIssue CitedMedium="Internet"><Volume>17</Volume><Issue>15</Issue><PubDate><Year>2020</Year><Month>07</Month><Day>28</Day></PubDate></JournalIssue><Title>International journal of environmental research and public health</Title><ISOAbbreviation>Int J Environ Res Public Health</ISOAbbreviation></Journal><ArticleTitle>Analyzing Spanish News Frames on Twitter during COVID-19-A Network Study of El País and El Mundo.</ArticleTitle><ELocationID EIdType="pii" ValidYN="Y">E5414</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.3390/ijerph17155414</ELocationID><Abstract><AbstractText>While COVID-19 is becoming one of the most severe public health crises in the twenty-first century, media coverage about this pandemic is getting more important than ever to make people informed. Drawing on data scraped from Twitter, this study aims to analyze and compare the news updates of two main Spanish newspapers <i>El País</i> and <i>El Mundo</i> during the pandemic. Throughout an automatic process of topic modeling and network analysis methods, this study identifies eight news frames for each newspaper's Twitter account. Furthermore, the whole pandemic development process is split into three periods-the pre-crisis period, the lockdown period and the recovery period. The networks of the computed frames are visualized by these three segments. This paper contributes to the understanding of how Spanish news media cover public health crises on social media platforms.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Yu</LastName><ForeName>Jingyuan</ForeName><Initials>J</Initials><Identifier Source="ORCID">0000-0002-9400-4859</Identifier><AffiliationInfo><Affiliation>Department of Social Psychology, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lu</LastName><ForeName>Yanqin</ForeName><Initials>Y</Initials><Identifier Source="ORCID">0000-0002-6925-0783</Identifier><AffiliationInfo><Affiliation>School of Media and Communication, Bowling Green State University, Bowling Green, OH 43403, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Muñoz-Justicia</LastName><ForeName>Juan</ForeName><Initials>J</Initials><Identifier 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PubStatus="accepted"><Year>2020</Year><Month>07</Month><Day>24</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>8</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32731359</ArticleId><ArticleId IdType="pii">ijerph17155414</ArticleId><ArticleId IdType="doi">10.3390/ijerph17155414</ArticleId><ArticleId IdType="pmc">PMC7432441</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Proc Natl Acad Sci U S A. 2016 Oct 18;113(42):11823-11828</Citation><ArticleIdList><ArticleId IdType="pubmed">27694580</ArticleId></ArticleIdList></Reference><Reference><Citation>Health Commun. 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