Using BERT to Improve Fake News Detection: A Transformer-Based Method

Abstract

The quick spread of fake news on digital platforms is a big threat to public trust, democratic processes, and the health of society as a whole. Keyword-based filtering and classical machine learning are examples of traditional detection methods that have not been able to handle the complexity and subtlety of modern misinformation. This paper suggests a better way to find fake news using BERT (Bidirectional Encoder Representations from Transformers), a deep learning model based on Transformers that can accurately capture the subtleties of language and the meaning of words in context. We fine-tune the model on benchmark datasets like LIAR, FakeNewsNet, and ISOT, and then we test it using metrics like precision, recall, F1-score, accuracy, and ROC-AUC. The BERT-based model does much better than traditional classifiers like Logistic Regression, SVM, CNN, and LSTM, with an accuracy of 92.4% and a ROC-AUC of 95.1%. This study goes into more detail about problems like computational complexity and changing patterns of false information. It also looks at possible future directions, such as using lightweight models, multimodal analysis, and explainable AI. The results show that BERT could be a good and scalable way to fight fake news in real life.