Leveraging Large Language Models for Rumours Detection in Social Media

Abstract

The rapid increase in content generated by users on social media has significantly accelerated the dissemination of rumours and misinformation, creating serious societal challenges. This paper introduces a robust and scalable framework for real-time rumour detection powered by Large Language Models, like BERT, RoBERTa, and GPTs-4. The proposed system combines Natural Language Processing techniques with sentiment analysis, stance detection, and automated fact-checking to enhance contextual understanding and assess credibility more effectively. Data is collected from various social media platforms like Twitter, Facebook, and Reddit, along with benchmark datasets like PHEME and FakeNewsNet.