{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T20:31:47Z","timestamp":1771273907565,"version":"3.50.1"},"reference-count":66,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T00:00:00Z","timestamp":1628208000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["LHT"],"published-print":{"date-parts":[[2022,11,22]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The purpose of this paper is to explore the features of health misinformation on social media sites (SMSs). The primary goal of the study is to investigate the salient features of health misinformation and to develop a tool of features to help users and social media companies identify health misinformation.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>Empirical data include 1,168 pieces of health information that were collected from WeChat, a dominant SMS in China, and the obtained data were analyzed through a process of open coding, axial coding and selective coding. Then chi-square test and analysis of variance (ANOVA) were adopted to identify salient features of health misinformation.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The findings show that the features of health misinformation on SMSs involve surface features, semantic features and source features, and there are significant differences in the features of health misinformation between different topics. In addition, the list of features was developed to identify health misinformation on SMSs.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>This study raises awareness of the key features of health misinformation on SMSs. It develops a list of features to help users distinguish health misinformation as well as help social media companies filter health misinformation.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>Theoretically, this study contributes to the academic discourse on health misinformation on SMSs by exploring the features of health misinformation. Methodologically, the paper serves to enrich the literature around health misinformation and SMSs that have hitherto mostly drawn data from health websites.<\/jats:p><\/jats:sec>","DOI":"10.1108\/lht-09-2020-0242","type":"journal-article","created":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T00:20:51Z","timestamp":1628209251000},"page":"1384-1401","source":"Crossref","is-referenced-by-count":25,"title":["Identifying features of health misinformation on social media sites: an exploratory analysis"],"prefix":"10.1108","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5792-877X","authenticated-orcid":false,"given":"Shuai","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0187-0131","authenticated-orcid":false,"given":"Feicheng","family":"Ma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8921-0826","authenticated-orcid":false,"given":"Yunmei","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8676-5519","authenticated-orcid":false,"given":"Wenjing","family":"Pian","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2021,8,6]]},"reference":[{"issue":"3","key":"key2022112909401590900_ref066","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1093\/jamia\/ocw140","article-title":"Toward automated assessment of health Web page quality using the DISCERN instrument","volume":"24","year":"2017","journal-title":"Journal of the American Medical Informatics Association"},{"issue":"1","key":"key2022112909401590900_ref001","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1108\/LHT-10-2016-0108","article-title":"Motivating issues affecting students' use of social media sites in Ghanaian tertiary institutions","volume":"36","year":"2018","journal-title":"Library Hi Tech"},{"issue":"7","key":"key2022112909401590900_ref002","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.2105\/AJPH.2016.303181","article-title":"Emotional feedback and the viral spread of social media messages about autism spectrum disorders","volume":"106","year":"2016","journal-title":"American Journal of Public Health"},{"issue":"4","key":"key2022112909401590900_ref003","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1287\/isre.2016.0648","article-title":"Creating value in online communities: the sociomaterial configuring of strategy, platform, and stakeholder engagement","volume":"27","year":"2016","journal-title":"Information Systems Research"},{"issue":"4","key":"key2022112909401590900_ref004","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1111\/jcom.12166","article-title":"In related news, that was wrong: the correction of misinformation through related stories functionality in social media","volume":"65","year":"2015","journal-title":"Journal of Communication"},{"issue":"9","key":"key2022112909401590900_ref005","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1080\/10410236.2017.1331312","article-title":"See something, say something: correction of global health misinformation on social media","volume":"33","year":"2018","journal-title":"Health Communication"},{"issue":"16","key":"key2022112909401590900_ref006","first-page":"e135","article-title":"Automated detection of HONcode website conformity compared to manual detection: an evaluation","volume":"17","year":"2015","journal-title":"Journal of Medical Internet Research"},{"key":"key2022112909401590900_ref007","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1016\/j.procs.2017.11.122","article-title":"How to sort trustworthy health online information? 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