Explore the Potential of LLMs in Misinformation Detection: An Empirical Study

• DOI: 10.48550/arxiv.2311.12699
• Publication Date: 2023-01-01
• Authors: Chen, Mengyang; Wei, Lingwei; Cao, Han; Zhou, Wei; Hu, Songlin
• Keywords: FOS: Computer and information sciences; Computer Science - Computers and Society; Computer Science - Computation and Language; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; Computers and Society (cs.CY); Computation and Language (cs.CL)

Large Language Models (LLMs) have garnered significant attention for their powerful ability in natural language understanding and reasoning. In this paper, we present a comprehensive empirical study to explore the performance of LLMs on misinformation detection tasks. This study stands as the pioneering investigation into the understanding capabilities of multiple LLMs regarding both content and propagation across social media platforms. Our empirical studies on eight misinformation detection datasets show that LLM-based detectors can achieve comparable performance in text-based misinformation detection but exhibit notably constrained capabilities in comprehending propagation structure compared to existing models in propagation-based misinformation detection. Our experiments further demonstrate that LLMs exhibit great potential to enhance existing misinformation detection models. These findings highlight the potential ability of LLMs to detect misinformation.