Explore the Potential of LLMs in Misinformation Detection: An Empirical Study
This work addresses misinformation detection for social media users, but it is incremental as it builds on existing models and datasets.
The paper conducted an empirical study to evaluate LLMs on misinformation detection tasks, finding that they achieve comparable performance in text-based detection but have limited capabilities in propagation-based detection compared to existing models.
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.