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<identifier>oai:dbk.gesis.org:SDN/10.7802_2437</identifier>
<datestamp>2023-03-11</datestamp>

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<dc:type>Dataset</dc:type>
<dc:title>SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse</dc:title>
<dc:creator>Hafid, Salim,  LIRMM, CNRS, University of Montpellier, Montpellier, France</dc:creator>
<dc:creator>Schellhammer, Sebastian,  GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany</dc:creator>
<dc:creator>Bringay, Sandra,  LIRMM, CNRS, University of Montpellier, Montpellier, France</dc:creator>
<dc:creator>Todorov, Konstantin,  LIRMM, CNRS, University of Montpellier, Montpellier, France</dc:creator>
<dc:creator>Dietze, Stefan,  GESIS - Leibniz Institute for the Social Sciences Cologne &amp; Heinrich-Heine-University D&#252;sseldorf, Germany</dc:creator>
<dc:identifier>10.7802/2437</dc:identifier>
<dc:identifier>10.7802/2437</dc:identifier>
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<dc:identifier>10.7802/2437</dc:identifier>
<dc:language>de</dc:language>
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<dc:description><![CDATA[This repository contains an expert-annotated dataset of 1261 tweets and the corresponding annotation framework from the publication "SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse" (https://arxiv.org/abs/2206.07360). The tweets are annotated with three different categories of science-relatedness: 
(1) Scientific knowledge (scientifically verifiable claims): Tweets that include a claim or a question that could be scientifically verified, (2) Reference to scientific knowledge: Tweets that include at least one reference to scientific knowledge (references can either be direct, e.g., DOI, title of a paper or indirect, e.g., a link to an article that includes a direct reference), and (3) Related to scientific research in general: Tweets that mention a scientific research context (e.g., mention a scientist, scientific research efforts, research findings).
Further, the annotations include the annotators' confidence scores as well as labels for compound claims and ironic tweets.]]></dc:description>
<dc:description><![CDATA[This repository contains an expert-annotated dataset of 1261 tweets and the corresponding annotation framework from the publication "SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse" (https://arxiv.org/abs/2206.07360). The tweets are annotated with three different categories of science-relatedness: 
(1) Scientific knowledge (scientifically verifiable claims): Tweets that include a claim or a question that could be scientifically verified, (2) Reference to scientific knowledge: Tweets that include at least one reference to scientific knowledge (references can either be direct, e.g., DOI, title of a paper or indirect, e.g., a link to an article that includes a direct reference), and (3) Related to scientific research in general: Tweets that mention a scientific research context (e.g., mention a scientist, scientific research efforts, research findings).
Further, the annotations include the annotators' confidence scores as well as labels for compound claims and ironic tweets.]]></dc:description>
<dc:contributor>GESIS Data Archive for the Social Sciences</dc:contributor>
<dc:publisher>GESIS Data Archive</dc:publisher>
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