SUMMARY
Using the architecture of neural_network models designed for text classification, the models can be categorized into LSTM, TextCNN, TextRNN, TextRCNN, Transformer, and ensemble learning-based hybrid network models. The properties and focuses of each neural_network model are presented in Table 1. Properties of various neural_network models. This paper has successively reproduced the rumor detection experiments of LSTM, TextCNN, TextRNN_Att, Transformer, and combined theory and practice to feel their respective advantages. In this paper, four types of models, TextCNN, Transformer, LSTM and TextRNN_Att, have been chosen to significantly enhance the precision of rumor detection. Model Name Transformer TextRNN_Att . . .
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