Research on Fake Information Detection Based on XLNet and Recurrent Neural Network Model
Fake information is widely spread on the Internet with the help of rapidly developing social media,so efficiently and accurately completing the task of fake information detection has become one of the research hotspots in the field of natural lan-guage processing in recent years.The existing fake information detection methods have the problems that the data training is not ac-curate enough and the model does not highlight the influence of key features.Aiming at this problem,this paper proposes a fake in-formation detection method based on XLNet and recurrent neural network model.This method encodes and extracts features based on the XLNet model,and combines the bidirectional GRU model to further capture the deep semantic features of the text.At the same time,an attention mechanism is introduced to assign different weights to different features in the text according to the impor-tance of the words.Complete semantic feature output of the text is classified for fake information detection.The experimental results show that the method achieves 94.6%and 96.3%accuracy on the Weibo public dataset and the COVID-19 Fake News dataset re-spectively,which can effectively identify fake information,and has certain guiding significance for the task of fake information de-tection.
text classificationfake information detectionXLNetattention mechanism
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