Open Access

ARTICLE

Analysis of Twitter Data Using Evolutionary Clustering during the COVID-19 Pandemic

Ibrahim Arpaci¹, Shadi Alshehabi², Mostafa Al-Emran^{3, *}, Mahmoud Khasawneh⁴, Ibrahim Mahariq⁴, Thabet Abdeljawad^{5, 6, 7}, Aboul Ella Hassanien^{8, 9}

1 Department of Computer Education and Instructional Technology, Tokat Gaziosmanpasa University, Tokat, Turkey.
2 Department of Computer Engineering, University of Turkish Aeronautical Association, Ankara, Turkey.
3 Applied Computational Civil and Structural Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh, Vietnam.
4 College of Engineering and Technology, American University of the Middle East, Kuwait, Kuwait.
5 Department of Mathematics and General Sciences, Prince Sultan University, Riyadh, 66833, Saudi Arabia.
6 Department of Medical Research, China Medical University, Taichung, 40402, Taiwan.
7 Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan.
8 Faculty of Computers and AI, Cairo University, Giza, Egypt.
9 Scientific Research Group in Egypt, Cairo, Egypt.

* Corresponding Author: Mostafa Al-Emran. Email: .

(This article belongs to the Special Issue: Mathematical aspects of the Coronavirus Disease 2019 (COVID-19): Analysis and Control)

Computers, Materials & Continua 2020, 65(1), 193-204. https://doi.org/10.32604/cmc.2020.011489

Received 11 May 2020; Accepted 05 June 2020; Issue published 23 July 2020

Abstract

People started posting textual tweets on Twitter as soon as the novel coronavirus (COVID-19) emerged. Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks. Therefore, this study aimed to analyze 43 million tweets collected between March 22 and March 30, 2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering analysis. The results indicated that unigram terms were trended more frequently than bigram and trigram terms. A large number of tweets about the COVID-19 were disseminated and received widespread public attention during the epidemic. The high-frequency words such as “death”, “test”, “spread”, and “lockdown” suggest that people fear of being infected, and those who got infection are afraid of death. The results also showed that people agreed to stay at home due to the fear of the spread, and they were calling for social distancing since they become aware of the COVID-19. It can be suggested that social media posts may affect human psychology and behavior. These results may help governments and health organizations to better understand the psychology of the public, and thereby, better communicate with them to prevent and manage the panic.

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