@Article{cmc.2020.011489,
AUTHOR = {Ibrahim Arpaci, Shadi Alshehabi, Mostafa Al-Emran, Mahmoud Khasawneh, Ibrahim Mahariq, Thabet Abdeljawad, Aboul Ella Hassanien},
TITLE = {Analysis of Twitter Data Using Evolutionary Clustering during  the COVID-19 Pandemic},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {193--204},
URL = {http://www.techscience.com/cmc/v65n1/39561},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.011489}
}