Weighted fine-tuned BERT-based sparse RNN for fake news detection

Abstract

Fake news refers to misinformation or false reports shared in the form of images, articles, or videos that are disguised as real news to try to manipulate people’s opinions. However, detection systems fail to capture diverse features of fake news due to variability in linguistic styles, contexts, and sources, which lead to inaccurate identification. For this purpose, a weighted fine-tuned-bidirectional encoder representation for transformer based sparse recurrent neural network (WFT-BERT-SRNN) is proposed for fake news detection using deep learning (DL). Initially, data is acquired from Buzzfeed PolitiFact, Fakeddit, and Weibo datasets to evaluate WFT BERT-SRNN. Pre-processing is established using stopword removal, tokenization, and stemming to eliminate unwanted phrases or words. Then, WFT-BERT is employed to extract features. Finally, SRNN is employed to detect and classify fake news as real or fake. Existing techniques like deep neural networks for Fake news detection (DeepFake), BERT with joint learning, and multi-EDU structure for Fake news detection (EDU4FD), Image caption-based technique, and fine-grained multimodal fusion network (FMFN) are compared with WFT-BERT-SRNN. The WFT-BERT-SRNN achieves a better accuracy of 0.9847, 0.9724, 0.9624, and 0.9725 for Buzzfeed, Politifact, Fakeddit, and Weibo datasets compared to existing techniques like DeepFake, BERT-joint framework, EDU4FD, Image caption-based technique, and FMFN.