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Analyzing Spanish News Frames on Twitter during COVID-19—A Network Study of El País and El Mundo

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 mai...

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Detalles Bibliográficos
Autores principales: Yu, Jingyuan, Lu, Yanqin, Muñoz-Justicia, Juan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7432441/
https://www.ncbi.nlm.nih.gov/pubmed/32731359
http://dx.doi.org/10.3390/ijerph17155414
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author Yu, Jingyuan
Lu, Yanqin
Muñoz-Justicia, Juan
author_facet Yu, Jingyuan
Lu, Yanqin
Muñoz-Justicia, Juan
author_sort Yu, Jingyuan
collection PubMed
description 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.
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spelling pubmed-74324412020-08-24 Analyzing Spanish News Frames on Twitter during COVID-19—A Network Study of El País and El Mundo Yu, Jingyuan Lu, Yanqin Muñoz-Justicia, Juan Int J Environ Res Public Health Article 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. MDPI 2020-07-28 2020-08 /pmc/articles/PMC7432441/ /pubmed/32731359 http://dx.doi.org/10.3390/ijerph17155414 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yu, Jingyuan
Lu, Yanqin
Muñoz-Justicia, Juan
Analyzing Spanish News Frames on Twitter during COVID-19—A Network Study of El País and El Mundo
title Analyzing Spanish News Frames on Twitter during COVID-19—A Network Study of El País and El Mundo
title_full Analyzing Spanish News Frames on Twitter during COVID-19—A Network Study of El País and El Mundo
title_fullStr Analyzing Spanish News Frames on Twitter during COVID-19—A Network Study of El País and El Mundo
title_full_unstemmed Analyzing Spanish News Frames on Twitter during COVID-19—A Network Study of El País and El Mundo
title_short Analyzing Spanish News Frames on Twitter during COVID-19—A Network Study of El País and El Mundo
title_sort analyzing spanish news frames on twitter during covid-19—a network study of el país and el mundo
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7432441/
https://www.ncbi.nlm.nih.gov/pubmed/32731359
http://dx.doi.org/10.3390/ijerph17155414
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