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The effect of official intervention on reducing the use of potentially discriminatory language during the Covid-19 pandemic in China

Bibliographic Data

ID22235757
AuthorsYiwei Jiang (Shanghai International Studies University), Hsin‐che Wu (0000-0002-9774-860X, Shandong University, corresponding author), Yihang Zuo (University of Edinburgh)
Year2023
Volume10
Issue1
Publication date2023-09-14
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueHumanities and Social Sciences Communications (JOURNAL)
Journal identifiersISSN: 2662-9992 • E-ISSN: 2662-9992
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1057/s41599-023-02071-5
OpenAlexW4386749369
LanguageEN
References cited19

This study used the Baidu Index to analyze searches using discriminatory language toward the source of the outbreak during the initial stages of the COVID-19 pandemic. The study found a strong correlation between official intervention and a reduction in searches using discriminatory language. Following the official naming of the virus bt the government, the general public understood that searches using discriminatory language should be avoided, and search behavior dramatically changed. Meanwhile, this study also found that official intervention also overcame the effects of local socioeconomic factors. Regions with higher levels of socioeconomic development and higher population density had a stronger discriminatory language in internet searches, while the opposite was the case for regions with lower CPI and unemployment rates. However, after the official intervention, these factors no longer had a significant effect. It is recommended that officials worldwide provide early official media guidance during infectious disease outbreaks to reduce the use of discriminatory language and ensure the delivery of transparent and effective information to the public

China · Demographic economics · Disease · Economic growth · Economic interventionism · Economics · Linguistics · Official statistics · Outbreak · Pandemic · Political science · Politics · Population · Socioeconomic status · Sociology · COVID-19 Pandemic Impacts · Demography · Law · Medicine · Misinformation and Its Impacts · Psychology

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Highly citedNo

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