Voters’ view of leaders during the Covid‐19 crisis

Quantitative analysis of keyword descriptions provides strength and direction of evaluations

Bibliographic Data

ID10763111
AuthorsAnnika Fredén (0000-0003-0820-8626, Department of Political, Historical, Religious and Cultural Studies Karlstad University Karlstad Sweden, corresponding author), Sverker Sikström (0000-0003-2644-9626, Department of Psychology Lund University Lund Sweden)
Year2021
Volume102
Issue5
Pages2170-2183
Publication date2021-09-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSocial Science Quarterly (JOURNAL)
Journal identifiersISSN: 0038-4941 • E-ISSN: 1540-6237
PublisherWiley (PUBLISHER • GB)
DOI10.1111/ssqu.13036
PMID34548706
OpenAlexW3190412713
LanguageEN
Citations received4
References cited17

Objectives: Previous research suggests that governments usually gain support during crises such as the Covid-19. However, these findings are based on rating scales that only allow us to measure the strength of this support. This article proposes a new measure of how voters evaluate Prime Ministers (PM) by asking for descriptive keywords that are analyzed by natural language processing. Methods: By collecting a representative sample of citizens' own key words describing their PM in 15 countries in Europe during the outbreak of Covid-19, and analyzing these by latent semantic analysis and a multiple OLS regression, we could quantify the strength and direction of voters' view. Results: The strength analysis supported previous studies that describing the PM with positive words was strongly associated with vote intention. Furthermore, a change in the direction of the attitudes from "good" to "honest" was found. A new finding was that the pandemic was associated with an increase in polarization. Conclusions: The keyword evaluation analysis provides opportunities of evaluating both strength and direction of voters' view of their PM, where we show new results related to increased polarization and shift in the direction of attitudes

Coronavirus disease 2019 (COVID-19) · Data mining · Descriptive statistics · Econometrics · Economics · Machine learning · Measure (data warehouse) · Natural language processing · Pandemic · Polarization (electrochemistry) · Political science · Regression analysis · Sentiment analysis · Statistics · Computational and Text Analysis Methods · Computer Science · Electoral Systems and Political Participation · Mathematics · Medicine · Psychology · Sentiment Analysis and Opinion Mining · Social Psychology

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