Web of things based social media fake news classification with feature extraction using pre-trained convoluted recurrent network with deep fuzzy learning

Abstract

Fake news has been surfacing often and in great quantity online recently due to burgeoning development of online social networks for various economic and political goals. Online social network users can quickly become infected by this fake news by using deceptive language, and it has already had a significant impact on offline culture. Finding bogus news quickly is a crucial step in raising the credibility of information in online social networks. This study proposes a fuzzy logic-based web of things categorization system for detection of fake news. Deep learning algorithms were used to perform the feature extraction. The first feature extraction step has been completed. Convoluted recurrent network that has been pre-trained (Pre-Tr_Conv_ReNet). High dimensionality indexing was applied following the extraction of the characteristics. The feature, whether it be an image or text, is then identified using a ranking method based on indexing metrics. The learned file has been annotated using deep fuzzy learning (De_Fuz_Lear) to distinguish fraudulent web content after this training. Then decision was taken and the fake news was detected. The simulation results give the classified outputs. For this obtained output the parametric analysis has been done.