Digital misinformation and fake news detection using WoT integration with Asian social networks fusion based feature extraction with text and image classification by machine learning architectures

Abstract

Fake News contains potentially false data that is verified. Concerns about author accountability are being addressed by organizations such as the House of Commons and the Crosscheck initiative. So here proposed system designs the web classification based on ontology with fuzzy logic. The feature extraction has been carried out using deep learning techniques. The initial feature extraction has been done using AE-CNN (Autoencoder with convolution neural network). After the characteristics were extracted, high dimensionality indexing was used. Then based on indexing measures ranking process takes place to identify the feature whether it is image or text. After this training part the trained file has been annotated with the fuzzy-based ontology rules to detect online fake news web content. Then the decision is taken and finally genetic algorithm-based deep learning is carried out for web classification. The simulation results have been analyzed regarding accuracy, precision, recall and F-1 score.