Extracting Major Topics of COVID-19 Related Tweets
This work addresses the need for better understanding public discourse on social media during the pandemic, but it is incremental as it applies an existing method to new data with added temporal analysis.
The study tackled the problem of analyzing spatial and temporal trends in COVID-19 related tweets during quarantine periods, using LDA topic modeling to extract and name 10 major topics such as 'reopening' and 'death cases', and found fascinating results from users' shifting focus over time.
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.