{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T09:57:21Z","timestamp":1763978241707,"version":"3.38.0"},"reference-count":18,"publisher":"China Science Publishing & Media Ltd.","issue":"1","license":[{"start":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:00:00Z","timestamp":1621814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,2,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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-TweetIds 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.<\/jats:p>","DOI":"10.1162\/dint_a_00101","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T20:34:17Z","timestamp":1621888457000},"page":"66-87","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":11,"title":["Public Emotional Diffusion over COVID-19 Related Tweets Posted by\n                    Major Public Health Agencies in the United States"],"prefix":"10.3724","volume":"4","author":[{"given":"Haixu","family":"Xi","sequence":"first","affiliation":[{"name":"School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China"},{"name":"School of Computer Engineering, Jiangsu University of Technology, Changzhou 213001, China"}]},{"given":"Chengzhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Yi","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Sheng","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Jiangsu University of Technology, Changzhou 213001, China"}]}],"member":"2026","published-online":{"date-parts":[[2022,2,3]]},"reference":[{"issue":"102187","key":"2022020307030671900_ref1","article-title":"Retweets of officials\u2019 alarming vs reassuring messages\n                        during the COVID-19 pandemic: Implications for crisis\n                        management","volume":"55","author":"Raghav\n                                Rao","year":"2020","journal-title":"International Journal of Information\n                        Management"},{"key":"2022020307030671900_ref2","doi-asserted-by":"crossref","first-page":"112","DOI":"10.20473\/jisebi.6.2.112-122","article-title":"Tweets responding to the Indonesian government's\n                        handling of COVID-19: Sentiment analysis using SVM with normalized poly\n                        Kernel","volume":"6","author":"Prastyo","year":"2020","journal-title":"Journal of Information Systems Engineering\n                        and Business Intelligence"},{"key":"2022020307030671900_ref3","doi-asserted-by":"crossref","DOI":"10.1101\/2020.06.22.20137505","volume-title":"An analysis of COVID-19 article dissemination by Twitter compared to\n                        citation rates","author":"Fabiano","year":"2020"},{"issue":"5","key":"2022020307030671900_ref4","doi-asserted-by":"crossref","first-page":"e19458","DOI":"10.2196\/19458","article-title":"COVID-19 and the 5G conspiracy theory: Social network\n                        analysis of Twitter data","volume":"22","author":"Ahmed","year":"2020","journal-title":"Journal of Medical\n                        Internet Research"},{"issue":"5","key":"2022020307030671900_ref5","doi-asserted-by":"crossref","first-page":"e18897","DOI":"10.2196\/18897","article-title":"Conversations and medical news frames on Twitter:\n                        Infodemiological study on COVID-19 in South Korea","volume":"22","author":"Park","year":"2020","journal-title":"Journal of Medical Internet Research"},{"issue":"24","key":"2022020307030671900_ref6","doi-asserted-by":"crossref","first-page":"8788","DOI":"10.1073\/pnas.1320040111","article-title":"Experimental evidence of massive-scale emotional contagion\n                        through social networks","volume":"111","author":"Kramer","year":"2014","journal-title":"Proceedings of the National\n                        Academy of Sciences of the United States of America"},{"issue":"6","key":"2022020307030671900_ref7","doi-asserted-by":"crossref","first-page":"102313","DOI":"10.1016\/j.ipm.2020.102313","article-title":"Effect of anger, anxiety, and sadness on the propagation\n                        scale of social media posts after natural disasters","volume":"57","author":"Li","year":"2020","journal-title":"Information Processing & Management"},{"key":"2022020307030671900_ref8","first-page":"413","article-title":"Sentiment propagation in social networks: A case study in\n                        LiveJournal","volume":"6007","author":"Zafarani","year":"2010","journal-title":"Advances in Social Computing, Lecture\n                        Notes in Computer Science"},{"issue":"5","key":"2022020307030671900_ref9","first-page":"966","article-title":"A multimodal theory of affect diffusion","volume":"141","author":"Peters","year":"2015","journal-title":"Psychological Bulletin 2015,"},{"key":"2022020307030671900_ref10","first-page":"331","volume-title":"Towards modeling fuzzy propagation for sentiment analysis in online\n                        social networks: A case study on TweetScope","author":"Trung","year":"2013"},{"issue":"4","key":"2022020307030671900_ref11","first-page":"76","article-title":"Research on the effect and features of emotional bias in the\n                        communication of Sina social news: A Web mining and empirical analysis based\n                        on Sina social news (in Chinese)","volume":"39","author":"Xu","year":"2017","journal-title":"Chinese Journal of\n                        Journalism & Communication"},{"key":"2022020307030671900_ref12","first-page":"550","volume-title":"Sentiment flow through hyperlink networks","author":"Miller","year":"2011"},{"key":"2022020307030671900_ref13","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.physa.2017.08.025","article-title":"An emotional contagion model for heterogeneous social media\n                        with multiple behaviors","volume":"490","author":"Xiong","year":"2018","journal-title":"Physica A Statistical\n                        Mechanics & Its Applications"},{"issue":"2","key":"2022020307030671900_ref14","article-title":"Factors affecting social media users\u2019 emotions\n                        regarding food safety issues: Content analysis of a debate among Chinese\n                        Weibo users on genetically modified food security","volume":"9","author":"Xiong","year":"2021","journal-title":"Healthcare (Basel)"},{"volume-title":"The future of public health","year":"1988","author":"Institute of Medicine (US) Committee for the Study of the Future of\n                        Public Health","key":"2022020307030671900_ref15"},{"issue":"2","key":"2022020307030671900_ref16","doi-asserted-by":"crossref","first-page":"e19273","DOI":"10.2196\/19273","article-title":"Tracking social media discourse about the COVID-19 pandemic:\n                        Development of a public coronavirus Twitter data set","volume":"6","author":"Chen","year":"2020","journal-title":"JMIR Public Health Surveillance"},{"key":"2022020307030671900_ref17","first-page":"216","volume-title":"VADER: A parsimonious rule-based model for sentiment analysis of\n                        social media text","author":"Hutto","year":"2014"},{"key":"2022020307030671900_ref18","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1080\/0022250X.1999.9990219","article-title":"The centrality of groups and classes","volume":"23","author":"Everett","year":"1999","journal-title":"Journal of Mathematical Sociology"}],"container-title":["Data 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