Deep fake news detection system based on concatenated and recurrent modalities

作者：

Highlights：

• This paper proposes a solution for fake news detection based on deep learning.

• The proposed deep learning approach comprises both sequential and recurrent modalities.

• The proposed sequential models includes CNNs and concatenated CNNs, while the recurrent includes LSTM and GRU models.

• Each model is compared with the other proposed models as well as the works in the literature.

摘要

•This paper proposes a solution for fake news detection based on deep learning.•The proposed deep learning approach comprises both sequential and recurrent modalities.•The proposed sequential models includes CNNs and concatenated CNNs, while the recurrent includes LSTM and GRU models.•Each model is compared with the other proposed models as well as the works in the literature.

论文关键词：Fake News Detection,Deep Learning,CNN,Concatenated CNN,LSTM,GRU,CNNs-LSTM

论文评审过程：Received 3 September 2021, Revised 22 May 2022, Accepted 20 June 2022, Available online 14 July 2022, Version of Record 18 July 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117953