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            <name>Title</name>
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                <text>Coronavirus</text>
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            <name>Description</name>
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                <text>Dominio científico: Coronavirus</text>
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          <name>Title</name>
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              <text>Research on Fine-Grained Classification of Rumors in Public Crisis ——Take the COVID-19 incident as an example</text>
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          <name>Creator</name>
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              <text>Chen Shuaipu</text>
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          <name>Description</name>
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              <text>[Purpose / Meaning] Rumors are frequent in the COVID-19 epidemic crisis. In order to unite the power of dispelling rumors of various media platforms to help to break the rumors in a timely and professional manner, this article has designed a new fine-grained classification of rumors about COVID-19 based on the BERT model. [Method / Process] Based on the rumor data of several mainstream rumor refuting platforms, the pre-training model of BERT was used to fine-tuning in the context of COVID-19 events to obtain the feature vector representation of the rumor sentence level to achieve fine-grained classification, and a comparative experiment was conducted with the TextCNN and TextRNN models. [Result / Conclusion] The results show that the classificationF1 value of the model designed in this paper reaches 98.34%, which is higher than the TextCNN and TextRNN models by 2%, indicating that the model in this paper has a good classification judgment ability for COVID-19 rumors, and provides certain reference value for promoting the coordinated refuting of rumors during the public crisis.</text>
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              <text>2020</text>
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          <name>Identifier</name>
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              <text>10.1051/e3sconf/202017902027</text>
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              <text>Epidemiology and Health</text>
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          <name>Publisher</name>
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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>
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              <text>Environmental sciences</text>
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