From Misinformation to Truth: Fake News Detection with Transformer-Based Models

Nowadays, technological advancements have significantly transformed information dissemination and consumption strategies. People acquire news through online portals such as social media and news websites. However, this change is also accompanied by the rapid spread of fake news based on false and misleading information. The main reason is that the coverage of digital platforms and social media is wide, and news can spread quickly without any authenticity verification. This problem poses a serious challenge to social trust, the ability to make wise decisions, and democratic stability. Therefore, in this study, we developed an advanced artificial intelligence (AI) model to detect fake news using Transformer (BERT, DistilBERT, and RoBERTa ) models and evaluated their performance based on indicators such as accuracy, recall, and F1 score. The results show that RoBERTa performs best due to the use of a large dataset and better contextual understanding and achieved an accuracy of 93.84%, a precision of 91.80%, a recall of 95.28%, and an F1 score of 90.82%.