﻿<?xml version="1.0" encoding="UTF-8"?>
<ArticleSet>
  <Article>
    <Journal>
      <PublisherName>Tabriz University of Medical Sciences</PublisherName>
      <JournalTitle>Health Promotion Perspectives</JournalTitle>
      <Issn>2228-6497</Issn>
      <Volume>11</Volume>
      <Issue>3</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2021</Year>
        <Month>08</Month>
        <DAY>18</DAY>
      </PubDate>
    </Journal>
    <ArticleTitle>Systematic evaluation of COVID-19 related Internet health rumors during the breaking out period of COVID-19 in China</ArticleTitle>
    <FirstPage>288</FirstPage>
    <LastPage>298</LastPage>
    <ELocationID EIdType="doi">10.34172/hpp.2021.37</ELocationID>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName>Ge</FirstName>
        <LastName>Pu</LastName>
        <Identifier Source="ORCID">https://orcid.org/0000-0002-9295-6389</Identifier>
      </Author>
      <Author>
        <FirstName>Liu</FirstName>
        <LastName>Jin</LastName>
        <Identifier Source="ORCID">https://orcid.org/0000-0003-2181-1251</Identifier>
      </Author>
      <Author>
        <FirstName>Han</FirstName>
        <LastName>Xiao</LastName>
      </Author>
      <Author>
        <FirstName>Wei</FirstName>
        <LastName>Shu-ting</LastName>
      </Author>
      <Author>
        <FirstName>He</FirstName>
        <LastName>Xi-zhe</LastName>
      </Author>
      <Author>
        <FirstName>Tang</FirstName>
        <LastName>Ying</LastName>
      </Author>
      <Author>
        <FirstName>Xu</FirstName>
        <LastName>Xin</LastName>
      </Author>
      <Author>
        <FirstName>Wang</FirstName>
        <LastName>Sheng-yuan</LastName>
      </Author>
      <Author>
        <FirstName>Bian</FirstName>
        <LastName>Ying</LastName>
        <Identifier Source="ORCID">https://orcid.org/0000-0002-1716-2925</Identifier>
      </Author>
      <Author>
        <FirstName>Wu</FirstName>
        <LastName>Yibo</LastName>
        <Identifier Source="ORCID">https://orcid.org/0000-0001-9607-313X</Identifier>
      </Author>
    </AuthorList>
    <PublicationType>Journal Article</PublicationType>
    <ArticleIdList>
      <ArticleId IdType="doi">10.34172/hpp.2021.37</ArticleId>
    </ArticleIdList>
    <History>
      <PubDate PubStatus="received">
        <Year>2021</Year>
        <Month>05</Month>
        <Day>23</Day>
      </PubDate>
      <PubDate PubStatus="accepted">
        <Year>2021</Year>
        <Month>07</Month>
        <Day>18</Day>
      </PubDate>
    </History>
    <Abstract>Background: To adapt the scientific evaluation tool for the confusion evaluation of health rumors and to test this tool to the confusion evaluation of coronavirus disease 2019 (COVID-19)-related health rumors on Chinese online platforms during the outbreak period of COVID-19in China. Methods: The design of our study was systematic evaluation of COVID-19-related health rumors. Retrieved from 7 rumor-repellent platforms, rumors about COVID-19 were collected during the publication from December 1, 2019, to February 6, 2020, and their origins were traced. Researchers evaluated rumors using the confusion evaluation tool in 6 dimensions(creators, evidence selection, evidence evaluation, evidence application, backing and publication platform, conflict of interest). Items were scored using a seven-point Likert scale. The scores were converted into percentages, and the median of rumors from different sources was compared with rank-sum test. Results: Our research included 127 rumors. Scores were converted to percentages, median and interquartile range are used to describe the data. The median score: creators 25.00%(interquartile range, IQR, 16.67-37.50%), evidence selection 27.78% (IQR, 13.89-44.44%),evidence evaluation 33.33% (IQR, 25.00-45.83%), evidence application 36.11% (IQR, 22.22-47.22%), backing and publication platform 8.33% (IQR, 4.17-20.83%), conflict of interest75.00% (IQR, 50.00-83.33%). Almost 40% rumors came from WeChat and the rumors with the lowest scores were concentrated on the WeChat platform. The rumors about prevention methods have relatively lower scores. Conclusion: Most rumors included were not highly confusing for evaluators of this project.WeChat is the "worst-hit area" of COVID-19 related health rumors. More than half rumors focus on the description of prevention methods, which reflects the panic, anxiety and blind conformity of the public under public health emergencies.</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">COVID-19</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Health rumor</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Systematic evaluation</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">China</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">The breaking out period of COVID-19</Param>
      </Object>
    </ObjectList>
  </Article>
</ArticleSet>