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2022 IEEE International Conference on Big Data, Big Data 2022 ; : 2370-2372, 2022.
Article in English | Scopus | ID: covidwho-2282867

ABSTRACT

The COVID-19 pandemic has affected public behavior in a variety of ways. Concerns about the spread of a hitherto unknown virus drove numerous changes in public behavior, including a greater tendency to self-isolate at home. In this study, we assigned numerical scores to key sentiments expressed in COVID-19-related posts on major social media platform Twitter to measure changes in public sentiment during the pandemic. We also examined the relationship between mobility in various locations around Japan and scores for sentiments such as dislike and fear. Our research provided evidence of a tendency for mobility to decline (i.e. for more people to self-isolate at home) roughly one month after scores for negative public sentiment regarding COVID-19 increased. Mobility is closely connected with a variety of economic activities, mainly in service industries. This suggests that the sentiment in Twitter postings on COVID-19 that we discuss in this study is a leading indicator of changes in mobility (the extent to which people self-isolate at home), demonstrating the effectiveness of Twitter data in forecasting short-term changes in economic activity during the pandemic. © 2022 IEEE.

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