期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Analysis of Twitter Data Using Evolutionary Clustering during the COVID-19 Pandemic 被引量:2
1
作者 Ibrahim Arpaci Shadi Alshehabi +4 位作者 mostafa al-emran Mahmoud Khasawneh Ibrahim Mahariq Thabet Abdeljawad Aboul Ella Hassanien 《Computers, Materials & Continua》 SCIE EI 2020年第10期193-203,共11页
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 s... 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. 展开更多
关键词 TWITTER social media evolutionary clustering COVID-19 CORONAVIRUS
在线阅读 下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部