Leveraging Large Language Models for Rumours Detection in Social Media

Abstract

The rapid increase in the content generated by users on the social media has significantly accelerated the dissemination of rumors and misinformation, creating serious societal challenges. This paper introduces a robust and scalable framework for real-time rumor 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. Experimental results show that LLMs significantly outperform traditional machine learning models, with GPT-4 achieving an accuracy of up to 94.7%. An ablation study further highlights the contribution of each module to the overall performance of GPT -4 for Rumor Detection. This framework offers a very comprehensive and flexible approach for countering misinformation across a range of languages and social environments.