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• W3044684479 abstract "People started posting textual tweets on Twitter as soon as the novel coronavirus (COVID-19) emerged. Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks. Therefore, this study aimed to analyze 43 million tweets collected between March 22 and March 30, 2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering analysis. The results indicated that unigram terms were trended more frequently than bigram and trigram terms. A large number of tweets about the COVID-19 were disseminated and received widespread public attention during the epidemic. The high-frequency words such as “death”, “test”, “spread”, and “lockdown” suggest that people fear of being infected, and those who got infection are afraid of death. The results also showed that people agreed to stay at home due to the fear of the spread, and they were calling for social distancing since they become aware of the COVID-19. It can be suggested that social media posts may affect human psychology and behavior. These results may help governments and health organizations to better understand the psychology of the public, and thereby, better communicate with them to prevent and manage the panic." @default.
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• W3044684479 date "2020-01-01" @default.
• W3044684479 modified "2023-10-02" @default.
• W3044684479 title "Analysis of Twitter Data Using Evolutionary Clustering during the COVID-19 Pandemic" @default.
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• W3044684479 doi "https://doi.org/10.32604/cmc.2020.011489" @default.
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