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      <src>https://www.socictopen.socict.org/files/original/dc7f4e1c121418a76b0915baabb7be25.pdf</src>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
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                <text>Coronavirus</text>
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            <name>Description</name>
            <description>An account of the resource</description>
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                <text>Dominio científico: Coronavirus</text>
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    <name>Text</name>
    <description>A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.</description>
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      <name>Dublin Core</name>
      <description>The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.</description>
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        <element elementId="50">
          <name>Title</name>
          <description>A name given to the resource</description>
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            <elementText elementTextId="59585">
              <text>Qualitative analysis of visual risk communication on twitter during the Covid-19 pandemic</text>
            </elementText>
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        <element elementId="39">
          <name>Creator</name>
          <description>An entity primarily responsible for making the resource</description>
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            <elementText elementTextId="59586">
              <text>Joanna Sleigh, Julia Amann, Manuel Schneider, Effy Vayena</text>
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        <element elementId="41">
          <name>Description</name>
          <description>An account of the resource</description>
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              <text>Abstract Background The Covid-19 pandemic is characterized by uncertainty and constant change, forcing governments and health authorities to ramp up risk communication efforts. Consequently, visuality and social media platforms like Twitter have come to play a vital role in disseminating prevention messages widely. Yet to date, only little is known about what characterizes visual risk communication during the Covid-19 pandemic. To address this gap in the literature, this study’s objective was to determine how visual risk communication was used on Twitter to promote the World Health Organisations (WHO) recommended preventative behaviours and how this communication changed over time. Methods We sourced Twitter’s 500 most retweeted Covid-19 messages for each month from January–October 2020 using Crowdbreaks. For inclusion, tweets had to have visuals, be in English, come from verified accounts, and contain one of the keywords ‘covid19’, ‘coronavirus’, ‘corona’, or ‘covid’. Following a retrospective approach, we then performed a qualitative content analysis of the 616 tweets meeting inclusion criteria. Results Our results show communication dynamics changed over the course of the pandemic. At the start, most retweeted preventative messages came from the media and health and government institutions, but overall, personal accounts with many followers (51.3%) predominated, and their tweets had the highest spread (10.0%, i.e., retweet count divided by followers). Messages used mostly photographs and images were found to be rich with information. 78.1% of Tweets contained 1–2 preventative messages, whereby ‘stay home’ and ‘wear a mask’ frequented most. Although more tweets used health loss framing, health gain messages spread more. Conclusion Our findings can inform the didactics of future crisis communication. The results underscore the value of engaging individuals, particularly influencers, as advocates to spread health risk messages and promote solidarity. Further, our findings on the visual characteristic of the most retweeted tweets highlight factors that health and government organisations should consider when creating visual health messages for Twitter. However, that more tweets used the emotive medium of photographs often combined with health loss framing raises concerns about persuasive tactics. More research is needed to understand the implications of framing and its impact on public perceptions and behaviours.</text>
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          <name>Date</name>
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              <text>2021</text>
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        <element elementId="49">
          <name>Subject</name>
          <description>The topic of the resource</description>
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              <text>Public health, covid-19, Pandemic, Risk communication, twitter, visuals</text>
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        <element elementId="43">
          <name>Identifier</name>
          <description>An unambiguous reference to the resource within a given context</description>
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            <elementText elementTextId="59590">
              <text>10.1186/s12889-021-10851-4</text>
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        <element elementId="48">
          <name>Source</name>
          <description>A related resource from which the described resource is derived</description>
          <elementTextContainer>
            <elementText elementTextId="59591">
              <text>Epidemiology and Health</text>
            </elementText>
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        <element elementId="45">
          <name>Publisher</name>
          <description>An entity responsible for making the resource available</description>
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              <text>Korean Society of Epidemiology</text>
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          <name>Coverage</name>
          <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
          <elementTextContainer>
            <elementText elementTextId="59593">
              <text>Public aspects of medicine</text>
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