Ibrahim Arpaci1, Shadi Alshehabi2, Mostafa Al-Emran3, *, Mahmoud Khasawneh4, Ibrahim Mahariq4, Thabet Abdeljawad5, 6, 7, Aboul Ella Hassanien8, 9
CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 193-204, 2020, DOI:10.32604/cmc.2020.011489
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… More >
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