The spread of fake news has become a major problem with its impact felt in journalism, commerce, and democracy. This has resulted in a loss of trust in media sources, leaving people uncertain. To combat this issue, researchers in the field of Artificial Intelligence have been exploring strategies for fake news detection. In previous studies, much emphasis was placed on identifying fake news in online reviews and social media posts.
This project aims to develop a neural network for fake news detection, using a deep Convolutional Neural Network (CNN) that incorporates Recurrent Neural Network (RNN) algorithms. Our model is designed to automatically learn discriminatory features for fake news classification through multiple hidden layers in the deep neural network. Instead of using manually created features, our approach allows for the extraction of multiple features at each layer in the network.