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Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing
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 inf...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9535225/ https://www.ncbi.nlm.nih.gov/pubmed/36217352 http://dx.doi.org/10.1007/s41060-022-00364-7 |
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author | Lu, Guang Businger, Martin Dollfus, Christian Wozniak, Thomas Fleck, Matthes Heroth, Timo Lock, Irina Lipenkova, Janna |
author_facet | Lu, Guang Businger, Martin Dollfus, Christian Wozniak, Thomas Fleck, Matthes Heroth, Timo Lock, Irina Lipenkova, Janna |
author_sort | Lu, Guang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9535225 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-95352252022-10-06 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 Int J Data Sci Anal Regular Paper 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. Springer International Publishing 2022-10-06 2023 /pmc/articles/PMC9535225/ /pubmed/36217352 http://dx.doi.org/10.1007/s41060-022-00364-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Regular Paper Lu, Guang Businger, Martin Dollfus, Christian Wozniak, Thomas Fleck, Matthes Heroth, Timo Lock, Irina Lipenkova, Janna Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing |
title | Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing |
title_full | Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing |
title_fullStr | Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing |
title_full_unstemmed | Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing |
title_short | Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing |
title_sort | agenda-setting for covid-19: a study of large-scale economic news coverage using natural language processing |
topic | Regular Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9535225/ https://www.ncbi.nlm.nih.gov/pubmed/36217352 http://dx.doi.org/10.1007/s41060-022-00364-7 |
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