Deep Learning and Transfer Learning Models-based Fake News Detection

Abstract

The spread of fake news in various online platforms has become a major problem that can influence public opinion and decision-making. Recent Artificial intelligence models using Deep learning architectures have shown promise solutions in detecting fake news. This report explores the effectiveness of deep learning and transfer learning models, including BERT, CNN, LSTM, Bi-LSTM, GRU, and Bi-GRU. The proposed approach combines the power of deep learning models with the transfer learning models to achieve better fake news detection accuracy. The evaluation metrics encompass Accuracy, Precision, Recall, and F1-score. The results show that the proposed approach on the ISOT dataset, outperforms existing methods and achieves high accuracy values in detecting fake news