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  <title>[ALOE] Recent activities on resource "Fooled by beautiful data: Visualization aesthetics bias trust in science, news, and social media"</title>
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  <author>
    <name>ALOE</name>
    <uri>http://aloe-project.de/AloeFeeds/action/atomFeedResourceActivities?resourceId=teBJS3k</uri>
    <email>aloe-noreply@dfki.uni-kl.de</email>
  </author>
  <contributor>
    <name>Martin</name>
    <uri>http://aloe-project.de/AloeView/action/userData?userId=lzwX6p3</uri>
  </contributor>
  <subtitle type="text">This feed contains recent activities on resource "Fooled by beautiful data: Visualization aesthetics bias trust in science, news, and social media" in [ALOE]</subtitle>
  <id>http://aloe-project.de/AloeFeeds/action/atomFeedResourceActivities?resourceId=teBJS3k</id>
  <entry>
    <title>User 'Martin' contributed resource 'Fooled by beautiful data: Visualization aesthetics bias trust in science, news, and social media'</title>
    <link rel="alternate" type="text/html" href="http://aloe-project.de/AloeView/action/resourceDetailed?resourceId=teBJS3k" />
    <author>
      <name>ALOE</name>
      <uri>http://aloe-project.de/AloeFeeds/action/atomFeedResourceActivities?resourceId=teBJS3k</uri>
      <email>aloe-noreply@dfki.uni-kl.de</email>
    </author>
    <contributor>
      <name>Martin</name>
      <uri>http://aloe-project.de/AloeView/action/userData?userId=lzwX6p3</uri>
    </contributor>
    <id>http://aloe-project.de/AloeFeeds/action/atomFeedResourceActivities?resourceId=teBJS3k:6:2022-08-15 21:05:48.0</id>
    <content type="html">August 15, 2022 9:05:00 PM CEST: User &lt;a href="http://aloe-project.de/AloeView/action/userData?userId=lzwX6p3"&gt;&lt;b&gt;Martin&lt;/b&gt;&lt;/a&gt; contributed resource &lt;a href="http://aloe-project.de/AloeView/action/resourceDetailed?resourceId=teBJS3k"&gt;&lt;b&gt;Fooled by beautiful data: Visualization aesthetics bias trust in science, news, and social media&lt;/b&gt;&lt;/a&gt; with the following metadata:&lt;br/&gt;&lt;br/&gt;&lt;i&gt;associatedDate&lt;/i&gt;: &lt;b&gt;&lt;/b&gt;&lt;br/&gt;&lt;i&gt;creator&lt;/i&gt;: &lt;b&gt;Lin, Chujun, and Mark A. Thornton&lt;/b&gt;&lt;br/&gt;&lt;i&gt;description&lt;/i&gt;: &lt;b&gt;Scientists, policymakers, and the public increasingly rely on data visualizations &amp;ndash; such as COVID tracking charts, weather forecast maps, and political polling graphs &amp;ndash; to inform important decisions. The aesthetic decisions of graph-makers may produce graphs of varying visual appeal, independent of data quality. Here we tested whether the beauty of a graph influences how much people trust it. Across three studies, we sampled graphs from social media, news reports, and scientific publications, and consistently found that graph beauty predicted trust. In a fourth study, we manipulated both the graph beauty and misleadingness. We found that beauty, but not actual misleadingness, causally affected trust. These findings reveal a source of bias in the interpretation of quantitative data and indicate the importance of promoting data literacy in education.&lt;/b&gt;&lt;br/&gt;&lt;i&gt;language&lt;/i&gt;: &lt;b&gt;&lt;/b&gt;&lt;br/&gt;&lt;i&gt;license&lt;/i&gt;: &lt;b&gt;http://creativecommons.org/licenses/by/3.0/&lt;/b&gt;&lt;br/&gt;&lt;i&gt;publisher&lt;/i&gt;: &lt;b&gt;&lt;/b&gt;&lt;br/&gt;&lt;i&gt;resourceType&lt;/i&gt;: &lt;b&gt;resource-bookmark&lt;/b&gt;&lt;br/&gt;&lt;i&gt;rightsHolder&lt;/i&gt;: &lt;b&gt;&lt;/b&gt;&lt;br/&gt;&lt;i&gt;tags&lt;/i&gt;: &lt;b&gt;visualization paper aesthetics bias&lt;/b&gt;&lt;br/&gt;&lt;i&gt;title&lt;/i&gt;: &lt;b&gt;Fooled by beautiful data: Visualization aesthetics bias trust in science, news, and social media&lt;/b&gt;&lt;br/&gt;</content>
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