Extracting Major Topics of COVID-19 Related Tweets
11th International Conference on Computer Engineering and Knowledge, ICCKE 2021
; : 25-29, 2021.
Article
in English
| Scopus | ID: covidwho-1788694
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. © 2021 IEEE.
Covid-19; LDA; natural language processing; Topic modeling; Twitter; Computer viruses; Data mining; Modeling languages; Natural language processing systems; Social networking (online); Viruses; Allocation algorithm; Hot topics; Latent Dirichlet allocation; Model method; Social distance; Spatial and temporal trends; Temporal trends; User focus; Statistics
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
11th International Conference on Computer Engineering and Knowledge, ICCKE 2021
Year:
2021
Document Type:
Article
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