Research on Fine-Grained Classification of Rumors in Public Crisis ——Take the COVID-19 incident as an example

摘要

[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.