{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T05:20:46Z","timestamp":1777526446241,"version":"3.51.4"},"reference-count":55,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T00:00:00Z","timestamp":1605139200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["INTR"],"published-print":{"date-parts":[[2020,11,12]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The purpose of this research is to investigate the usage characteristics and the information propagation patterns of Chinese microblogs in different stages of an epidemic, given that the microblogging in China is different from other parts of the world. In addition, the authors aim to conceptualize the roles of different users and provide insights for using microblogging platforms to disseminate information in this context.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>The authors conducted an analysis on Sina Weibo microblogs about the African Swine Fever epidemic from August to October 2018. The authors firstly applied a label propagation algorithm to classify users into government, media, verified users and nonverified users. The authors analyzed several user metrics, traced the information propagation patterns of their microblogs and calculated the average speed of information propagation using computational approaches.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The authors\u2019 findings show that different types of users played different roles, such as supplying information, amplifying information, relaying information and engaging with other users. The microblogs posted by media dominated the propagation in most cases, but general users can propagate information faster. The direction of information propagation is one-way for the majority of microblogs, and few users repost earlier information. Additionally, microblogs attract more attention at the beginning and the middle phases of an epidemic. In the context of managing epidemics, the authors recommend governments and other verified users can work together to use microblogging platforms efficiently.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This research is one of the few studies to investigate information propagation patterns of different user categories on a Chinese microblogging platform during an epidemic. The authors\u2019 work can be used by government agencies and public health authorities for disseminating information efficiently during epidemics or emergencies, especially in the early stages.<\/jats:p><\/jats:sec>","DOI":"10.1108\/intr-11-2019-0490","type":"journal-article","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T13:13:29Z","timestamp":1605186809000},"page":"540-561","source":"Crossref","is-referenced-by-count":8,"title":["Roles of information propagation of Chinese microblogging users in epidemics: a crisis management perspective"],"prefix":"10.1108","volume":"31","author":[{"given":"Li","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8820-5443","authenticated-orcid":false,"given":"Patrick Cheong-Iao","family":"Pang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yain-Whar","family":"Si","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"issue":"4","key":"key2021031007452632200_ref001","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1007\/s10796-017-9789-4","article-title":"Institutional vs. Non-institutional use of social media during emergency response: a case of Twitter in 2014 Australian bush fire","volume":"20","year":"2018","journal-title":"Information Systems Frontiers"},{"issue":"1","key":"key2021031007452632200_ref002","first-page":"4:1","article-title":"Aggregate characterization of user behavior in Twitter and analysis of the retweet graph","volume":"15","year":"2015","journal-title":"ACM Transactions on Internet Technology"},{"key":"key2021031007452632200_ref003","volume-title":"Pattern Recognition and Machine Learning","year":"2006"},{"issue":"1","key":"key2021031007452632200_ref004","first-page":"993","article-title":"Latent Dirichlet allocation","volume":"3","year":"2003","journal-title":"Journal of Machine Learning Research"},{"key":"key2021031007452632200_ref005","first-page":"1","article-title":"Tweet, tweet, retweet: conversational aspects of retweeting on twitter","year":"2010"},{"issue":"6","key":"key2021031007452632200_ref006","first-page":"1661","article-title":"Crises and crisis management: integration, interpretation, and research development","volume":"43","year":"2016","journal-title":"Journal of Management"},{"key":"key2021031007452632200_ref007","volume-title":"Follow Whom? 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