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- W3044800712 abstract "[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 classification F 1 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." @default.
- W3044800712 created "2020-07-29" @default.
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- W3044800712 date "2020-01-01" @default.
- W3044800712 modified "2023-10-18" @default.
- W3044800712 title "Research on Fine-Grained Classification of Rumors in Public Crisis ——Take the COVID-19 incident as an example" @default.
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- W3044800712 doi "https://doi.org/10.1051/e3sconf/202017902027" @default.
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