{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T06:24:14Z","timestamp":1777875854794,"version":"3.51.4"},"reference-count":53,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2021,9,11]],"date-time":"2021-09-11T00:00:00Z","timestamp":1631318400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Glob Health Promot"],"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:p>The prevalence of health misinformation on social media could significantly influence individuals\u2019 health behaviors. To examine the prevalent topics, propagation, and correction of coronavirus disease 2019 (COVID-19) misinformation, automated content analyses were conducted for posts on Sina Weibo, which is China\u2019s largest microblogging site. In total, 177,816 posts related to COVID-19 misinformation during the COVID-19 outbreak in China were analyzed. The structural topic modeling identified 23 valid topics regarding COVID-19 misinformation and its correction, which were further categorized into three general themes. Sentiment analysis was conducted to generate positive and negative sentiment scores for each post. The zero-inflated Poisson model indicated that only the negative sentiment was a significant predictor of the number of comments (\u03b2 = 0.003, p &lt; 0.001) but not reposts. Furthermore, users are more prone to repost and comment on information regarding prevention\/treatment (e.g., traditional Chinese medicine preventing COVID) as well as potential threats of COVID-19 (e.g., COVID-19 was defined as an epidemic by World Health Organization). Health education and promotion implications are discussed.<\/jats:p>","DOI":"10.1177\/17579759211035053","type":"journal-article","created":{"date-parts":[[2021,9,11]],"date-time":"2021-09-11T07:06:35Z","timestamp":1631343995000},"page":"44-52","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["Understanding the landscape and propagation of COVID-19 misinformation and its correction on Sina Weibo"],"prefix":"10.1177","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1358-8096","authenticated-orcid":false,"given":"Qinghua","family":"Yang","sequence":"first","affiliation":[{"name":"Texas Christian University, Fort Worth, Texas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhifan","family":"Luo","sequence":"additional","affiliation":[{"name":"University at Albany, State University of New York, Albany, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muyang","family":"Li","sequence":"additional","affiliation":[{"name":"York University, Toronto, Ontario, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiangmeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Seattle University, Seattle, Washington, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2021,9,11]]},"reference":[{"key":"bibr1-17579759211035053","doi-asserted-by":"publisher","DOI":"10.2196\/11515"},{"key":"bibr2-17579759211035053","doi-asserted-by":"publisher","DOI":"10.1177\/1529100612451018"},{"key":"bibr3-17579759211035053","doi-asserted-by":"publisher","DOI":"10.7560\/314555-002"},{"key":"bibr4-17579759211035053","doi-asserted-by":"publisher","DOI":"10.1080\/10584609.2015.1102187"},{"key":"bibr5-17579759211035053","doi-asserted-by":"publisher","DOI":"10.1006\/enrs.1998.3940"},{"key":"bibr6-17579759211035053","doi-asserted-by":"publisher","DOI":"10.1177\/1075547020959670"},{"key":"bibr7-17579759211035053","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-021-01056-1"},{"key":"bibr8-17579759211035053","doi-asserted-by":"publisher","DOI":"10.3961\/jpmph.20.094"},{"key":"bibr9-17579759211035053","doi-asserted-by":"publisher","DOI":"10.1177\/003335491613100312"},{"key":"bibr10-17579759211035053","unstructured":"Pew Research Center. 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