摘要
Since the end of 2019,the COVID-19 outbreak worldwide has not only presented challenges for government agencies in addressing public health emergency,but also tested their capacity in dealing with public opinion on social media and responding to social emergencies.To understand the impact of COVID-19 related tweets posted by the major public health agencies in the United States on public emotion,this paper studied public emotional diffusion in the tweets network,including its process and characteristics,by taking Twitter users of four official public health systems in the United States as an example.We extracted the interactions between tweets in the COVID-19-Tweet Ids data set and drew the tweets diffusion network.We proposed a method to measure the characteristics of the emotional diffusion network,with which we analyzed the changes of the public emotional intensity and the proportion of emotional polarity,investigated the emotional influence of key nodes and users,and the emotional diffusion of tweets at different tweeting time,tweet topics and the tweet posting agencies.The results show that the emotional polarity of tweets has changed from negative to positive with the improvement of pandemic management measures.The public’s emotional polarity on pandemic related topics tends to be negative,and the emotional intensity of management measures such as pandemic medical services turn from positive to negative to the greatest extent,while the emotional intensity of pandemic related knowledge changes the most.The tweets posted by the Centers for Disease Control and Prevention and the Food and Drug Administration of the United States have a broad impact on public emotions,and the emotional spread of tweets’polarity eventually forms a very close proportion of opposite emotions.
基金
supported by Humanities and Social Science Research Fund of the Ministry of Education in China(Grant No.18YJC840045)
Jiangsu Social Science Fund(No.20TQA001)
作者简介
Haixu Xi,is currently a doctoral student in the School of Economics and Management,Nanjing University of Science and Technology and an Associate Professor of School of Computer Engineering,Jiangsu University of Technology,China.He received his Master’s degree from Nanjing Normal University in 2006.His research interests mainly focus on social media mining,big data application and natural language processing.ORCID:0000-0003-4099-9819;Corresponding author:Chengzhi Zhang,is a Professor of Department of Information Management,Nanjing University of Science and Technology,China.He received his Ph.D.degree of Information Science from Nanjing University,China.He has published more than 100 publications in journals,Including Journal of the Association for Information Science and Technology(JASIST),Aslib Journal of Information Management,Journal of Informetrics,Online Information Review,and Scientometrics,and conferences,including Annual Meeting of the Association for Computational Linguistics(ACL)and Annual Conference of the North American Chapter of the Association for Computational Linguistics(NAACL).His current research interests include scientific text mining,knowledge entity extraction and evaluation,and social media mining.ORCID:0000-0001-9522-2914,Email:zhangcz@njust.edu.cn;Yi Zhao,is currently a doctoral student in the School of Economics and Management,Nanjing University of Science and Technology.He received his Master’s degree from Hohai University in 2019.His research interests mainly focus on knowledge organization,science of science and natural language processing.ORCID:0000-0003-1050-7051;Sheng He,is a Professor of School of Computer Engineering,Jiangsu University of Technology,China.He received his B.S.degree in Physics from Fuyang Normal University in 1993 and M.E.degree in Software Engineering from University of Science and Technology of China in 2005.His research interests include data mining and big data application.ORCID:0000-0001-6762-8271
