| Autor: |
Chen Shuaipu |
| Jazyk: |
English<br />French |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
E3S Web of Conferences, Vol 179, p 02027 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2267-1242 |
| DOI: |
10.1051/e3sconf/202017902027 |
| Popis: |
[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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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