A hybrid deep learning model based on CNN-BiLSTM for rumor detection

The evolution of the World Wide Web and the rapid enhancement of social sites like Facebook, Twitter changed the way of communication. With these social media sites, people are creating and disseminating more information, meanwhile large rumor and fake news also increased. Automatic text classification as information and disinformation is a challenging task. We investigate the rumor identification problem by considering the contextual information. The proposed work is the hybridization of CNN and BILSTM with Glove embedding to classify the tweets into rumor and non-rumor. All the experiments are performed on publicly available dataset collected from Kaggle, that is the world largest community. Experimental result show that the proposed model outperformed when compared with baseline model. The proposed model provide 90.93% accuracy.