Voters' view of leaders during the Covid-19 crisis : Quantitative analysis of keyword descriptions provides strength and direction of evaluations
| Autor: | Sverker Sikström, Annika Fredén |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Covid‐19
Prime Ministers Download Latent semantic analysis Political Science Statsvetenskap Polarization (politics) Warranty approval ratings General Social Sciences Advertising Original Articles Permission quantitative text analysis Quantitative analysis (finance) Ordinary least squares Original Article Covid-19 sympathy score Natural language |
| Zdroj: | Social Science Quarterly |
| Popis: | Objectives Methods Results Conclusions 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.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.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.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. [ABSTRACT FROM AUTHOR] Copyright of Social Science Quarterly (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Databáze: | OpenAIRE |
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