TITLE:
Media Reports of the COVID-19 Pandemic: A Computational Text Analysis of English Reports in China, the UK, and the US
AUTHORS:
Shuran Yang, Wenxiang Zhang, Zhao Yuan
KEYWORDS:
Computational Text Analysis, COVID-19, Cluster Analysis, Sentiment Analysis, Media Studies
JOURNAL NAME:
Advances in Journalism and Communication,
Vol.9 No.2,
May
11,
2021
ABSTRACT: This study explored how news outlets, China Daily (CD), Cable News Network (CNN), and Daily Mail (DM) have reported the COVID-19 pandemic. Mainstream media is a credible communication path to guide public attention on COVID-19. Computational text analysis contributes to understanding media activities about the pandemic and promotes health information communication. The word frequency statistics and lexical diversity highlighted how pandemic reports changed in the early outbreak. A cluster analysis illustrated the frequency and semantic relationship between the highly frequent words from CD, CNN, and DM reports. Sentiment analysis was based on natural language processing when analyzing the sentiment of all headlines and the sentiment of the different words in the headlines. This study also discussed similarities and differences in the coverage by the three different media outlets at various stages of the outbreak. Three media reported comprehensive coverage of the pandemic. Since they are based in different countries, their focus and the numbers of reports are different at different stages. The richness of the vocabulary and the degree of emotion are related to their media attributes. These results can help health departments exchange information, guide accurate public awareness, and eliminate public fears regarding misconceptions about the pandemic.