Evidence map›Paper›PMID 36217352›Full record

ArticleInternational journal of data science and analytics2023

Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing.

Guang Lu, Martin Businger, Christian Dollfus, Thomas Wozniak, Matthes Fleck, Timo Heroth, Irina Lock, Janna Lipenkova

Abstract readNews
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of data science and analytics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Guang LuInstitute of Communication and Marketing, Lucerne University of Applied Sciences and Arts, Zentralstrasse 9, Lucerne, 6002 Switzerland.ORCID 0000-0003-2639-8785
Martin BusingerInstitute of Language Competence, ZHAW Zurich University of Applied Sciences, Theaterstrasse 17, Winterthur, 8401 Switzerland.
Christian DollfusInstitute of Communication and Marketing, Lucerne University of Applied Sciences and Arts, Zentralstrasse 9, Lucerne, 6002 Switzerland.
Thomas WozniakInstitute of Communication and Marketing, Lucerne University of Applied Sciences and Arts, Zentralstrasse 9, Lucerne, 6002 Switzerland.
Matthes FleckInstitute of Communication and Marketing, Lucerne University of Applied Sciences and Arts, Zentralstrasse 9, Lucerne, 6002 Switzerland.
Timo HerothInstitute of Financial Services Zug, Lucerne University of Applied Sciences and Arts, Zentralstrasse 9, Lucerne, 6002 Switzerland.
Irina LockInstitute of Communication Science, Friedrich Schiller University Jena, Ernst-Abbe-Platz 8, Jena, 07743 Germany.
Janna LipenkovaAnacode GmbH, Kurfürstendamm 76, Berlin, 10709 Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Big data analysisBusiness and societyCOVID-19Economic impactNatural language processing (NLP)News media coverage

Identifiers

PMID36217352
PMCPMC9535225

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.