计算机与数字工程2024，Vol.52Issue(6)：1754-1758,1853,6.DOI:10.3969/j.issn.1672-9722.2024.06.027
基于XLNet和循环神经网络模型的虚假信息检测研究
Research on Fake Information Detection Based on XLNet and Recurrent Neural Network Model
白致屹 1薛涛1
作者信息
- 1. 西安工程大学计算机科学学院 西安 710000
- 折叠
摘要
Abstract
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.关键词
文本分类/虚假信息检测/XLNet/注意力机制Key words
text classification/fake information detection/XLNet/attention mechanism分类
信息技术与安全科学引用本文复制引用
白致屹,薛涛..基于XLNet和循环神经网络模型的虚假信息检测研究[J].计算机与数字工程,2024,52(6):1754-1758,1853,6.
