  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.
