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Exploring Public Sentiment During COVID-19: A Cross Country Analysis
Ieee Transactions on Computational Social Systems ; : 12, 2022.
Article in English | Web of Science | ID: covidwho-1714076
ABSTRACT
COVID-19 has spread all over the world, accounting for countless death and enormous economic loss. Since the World Health Organization (WHO) declared COVID-19 as a pandemic, governments from different countries have made various policies to prevent the pandemic from becoming worse. However, civilian reactions to the pandemic vary when they face similar situations. This behavioral variation creates a challenge when it comes to policy-making. Such differences are generally implicit, hidden in ones' social lives. As a result, it is challenging to analyze such differences when the governments make policies. In this work, we investigate social media posts on Twitter and Weibo in order to effectively explore the difference in reactions across various countries, with the aim to understand national differences. To this end, we employ natural language processing (NLP) methods and Linguistic Inquiry and Word Count (LIWC) tools to process six languages in different countries, including the USA, Germany, France, Italy, the U.K., and China. We provide a comprehensive analysis of public reaction differences from the emotional perspective. Our findings verify that the reactions vary noticeably among various countries for some policies. Therefore, sentiment analysis can significantly influence policy-making. Our work sheds light on the mechanism of detecting the reaction differences in various countries, which can be utilized to conduct effective communication and make appropriate policy decisions.
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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Randomized controlled trials Language: English Journal: Ieee Transactions on Computational Social Systems Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Randomized controlled trials Language: English Journal: Ieee Transactions on Computational Social Systems Year: 2022 Document Type: Article