Abstract

The rapid spread of misinformation on social media platforms poses significant challenges to public discourse, political stability, and public health. Natural Language Processing (NLP) techniques, particularly deep learning models, have emerged as powerful tools for detecting and mitigating misinformation in real time. This paper explores the application of transformer-based models such as BERT, RoBERTa, and GPT-3 for misinformation detection, evaluating their performance on benchmark datasets like FakeNewsNet and LIAR. Our experiments demonstrate that fine-tuned transformer models achieve over 90% accuracy in classifying fake news. Additionally, we discuss deployment strategies for real-time social media monitoring, ethical considerations, and future research directions.