Detecting Fake News using Advanced Language Models: BERT and RoBERTa

The fake news story refers to information falsified or made up to manipulate or deceive the public. It is a serious issue that undermines the credibility of information sources and influences public opinion, discourse, and important decisions and outcomes. The content and source of news articles and stories must be considered when identifying and classifying fake news. Detecting fake news is an important and active research area with many potential applications and implications for society. Many challenges are involved in determining whether a news article is authentic. Several deep learning models that employ natural language processing (NLP) have shown excellent results in detecting fake news. To assess the truthfulness of news articles, our methodology is based on state-of-the-art language models based on transformers. Bidirectional Encoder Representation from Transformers (BERT) and Robustly Optimized BERT Technique (RoBERTa) is one of the most advanced models. Our findings reveal that the BERT model achieved an accuracy of 64%, while the RoBERTa model slightly outperformed it with an accuracy of 66%. These results are particularly significant when compared to similar research in this domain, which reported a maximum accuracy of 62% for both models on Liar dataset.