International Conference on Computer and Knowledge Engineering

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Extracting Major Topics of COVID-19 Related Tweets

Authors :

Faezeh Azizi¹ Hamed Vahdat-Nejad² Hamideh Hajiabadi³ Mohammad Hossein Khosravi⁴

1- Perlab, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran 2- Perlab, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran 3- Department of Computer Engineering, Birjand University of Technology, Iran 4- Faculty of Electrical and Computer Engineering, University of Birjand, Iran

Keywords :

topic modeling, covid-19, Twitter, natural language processing, LDA

Abstract :

Twitter is one of the most popular social networks in the last decade and is gathered millions of users around the world. 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, few attentions have been devoted to presenting and analyzing the spatial and temporal trends of Covid-19 topics. In this study, we use the topic modeling method on Covid-19-related tweets to address global topics during the nationwide quarantine periods (March 23 to June 23, 2020). 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. For extracting tweets' location, we operate the location dictionary-based method. 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 about topics over time.

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