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• W3204648175 abstract "With the outbreak of the Covid-19 virus, the activity of users on Twitter has significantly increased. Some studies have investigated the hot topics of tweets in this period; however, little attention has been paid to presenting and analyzing the spatial and temporal trends of Covid-19 topics. In this study, we use the topic modeling method to extract global topics during the nationwide quarantine periods (March 23 to June 23, 2020) on Covid-19 tweets. We implement the Latent Dirichlet Allocation (LDA) algorithm to extract the topics and then name them with the reopening, death cases, telecommuting, protests, anger expression, masking, medication, social distance, second wave, and peak of the disease titles. We additionally analyze temporal trends of the topics for the whole world and four countries. By analyzing the graphs, fascinating results are obtained from altering users' focus on topics over time." @default.
• W3204648175 created "2021-10-11" @default.
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• W3204648175 date "2021-10-05" @default.
• W3204648175 modified "2023-09-27" @default.
• W3204648175 title "Extracting Major Topics of COVID-19 Related Tweets." @default.
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