Transfer Learning Driven Fake News Detection and Classification using Large Language Models

Today, the problem of using social media to spread false information is not only widespread but also quite serious. The extensive dissemination of fake news, regardless of whether it is produced by human beings or computer programs, has a negative impact not only on society but also on individuals in terms of politics and society. Currently of social networks, the quick dissemination of news provides a challenge when it comes to establishing the reliability of the information in a satisfactory manner. Because of this, the requirement for automated technologies that can identify fake news has become of the utmost importance. In this work, the application of transfer learning with several different large language models was utilized, which resulted in an improvement in the efficiency of decision-making about bogus news. In addition, we study several different words embedding techniques, such as Word2Vec and one-hot encoding, and we introduce the capacity of transfer learning to deep learning and machine learning algorithms for the aim of comparing their respective levels of performance. Based on the findings of our experiments conducted on two datasets derived from the real world, we have determined that the transfer learning-based strategy that we have developed can outperform the most advanced approaches by a minimum of 3.9% in terms of accuracy and offering a rational explanation.