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Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing.
Lu, Guang; Businger, Martin; Dollfus, Christian; Wozniak, Thomas; Fleck, Matthes; Heroth, Timo; Lock, Irina; Lipenkova, Janna.
  • Lu G; Institute of Communication and Marketing, Lucerne University of Applied Sciences and Arts, Zentralstrasse 9, Lucerne, 6002 Switzerland.
  • Businger M; Institute of Language Competence, ZHAW Zurich University of Applied Sciences, Theaterstrasse 17, Winterthur, 8401 Switzerland.
  • Dollfus C; Institute of Communication and Marketing, Lucerne University of Applied Sciences and Arts, Zentralstrasse 9, Lucerne, 6002 Switzerland.
  • Wozniak T; Institute of Communication and Marketing, Lucerne University of Applied Sciences and Arts, Zentralstrasse 9, Lucerne, 6002 Switzerland.
  • Fleck M; Institute of Communication and Marketing, Lucerne University of Applied Sciences and Arts, Zentralstrasse 9, Lucerne, 6002 Switzerland.
  • Heroth T; Institute of Financial Services Zug, Lucerne University of Applied Sciences and Arts, Zentralstrasse 9, Lucerne, 6002 Switzerland.
  • Lock I; Institute of Communication Science, Friedrich Schiller University Jena, Ernst-Abbe-Platz 8, Jena, 07743 Germany.
  • Lipenkova J; Anacode GmbH, Kurfürstendamm 76, Berlin, 10709 Germany.
Int J Data Sci Anal ; : 1-22, 2022 Oct 06.
Article in English | MEDLINE | ID: covidwho-2299152
ABSTRACT
Over the past two years, organizations and businesses have been forced to constantly adapt and develop effective responses to the challenges of the COVID-19 pandemic. The acuteness, global scale and intense dynamism of the situation make online news and information even more important for making informed management and policy decisions. This paper focuses on the economic impact of the COVID-19 pandemic, using natural language processing (NLP) techniques to examine the news media as the main source of information and agenda-setters of public discourse over an eight-month period. The aim of this study is to understand which economic topics news media focused on alongside the dominant health coverage, which topics did not surface, and how these topics influenced each other and evolved over time and space. To this end, we used an extensive open-source dataset of over 350,000 media articles on non-medical aspects of COVID-19 retrieved from over 60 top-tier business blogs and news sites. We referred to the World Economic Forum's Strategic Intelligence taxonomy to categorize the articles into a variety of topics. In doing so, we found that in the early days of COVID-19, the news media focused predominantly on reporting new cases, which tended to overshadow other topics, such as the economic impact of the virus. Different independent news sources reported on the same topics, showing a herd behavior of the news media during this global health crisis. However, a temporal analysis of news distribution in relation to its geographic focus showed that the rise in COVID-19 cases was associated with an increase in media coverage of relevant socio-economic topics. This research helps prepare for the prevention of social and economic crises when decision-makers closely monitor news coverage of viruses and related topics in other parts of the world. Thus, monitoring the news landscape on a global scale can support decision-making in social and economic crises. Our analyses point to ways in which this monitoring and issues management can be improved to remain alert to social dynamics and market changes.
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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Int J Data Sci Anal Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Int J Data Sci Anal Year: 2022 Document Type: Article