In this work, we investigate to use Large Language Models (LLMs) for rumor detection on social media. However, it is challenging for LLMs to reason over the entire propagation information on social media, which contains news contents and numerous comments, due to LLMs may not concentrate on key clues in the complex propagation information, and have trouble in reasoning when facing massive and redundant information. Accordingly, we propose an LLM-empowered Rumor Detection (LeRuD) approach, in which we design prompts to teach LLMs to reason over important clues in news and comments, and divide the entire propagation information into a Chain-of-Propagation for reducing LLMs' burden. We conduct extensive experiments on the Twitter and Weibo datasets, and LeRuD outperforms several state-of-the-art rumor detection models by 3.2% to 7.7%. Meanwhile, by applying LLMs, LeRuD requires no data for training, and thus shows more promising rumor detection ability in few-shot or zero-shot scenarios.

翻译：本研究探讨了利用大型语言模型（LLMs）检测社交媒体谣言的方法。然而，由于社交媒体上的传播信息包含新闻内容与大量评论，LLMs难以对完整传播信息进行推理：其可能无法聚焦复杂传播信息中的关键线索，且在面对海量冗余信息时存在推理困难。为此，我们提出了一种基于LLM驱动的谣言检测方法（LeRuD），该方法通过设计提示词引导LLMs对新闻与评论中的关键线索进行推理，并将完整传播信息划分为传播链（Chain-of-Propagation）以降低LLMs的推理负担。我们在Twitter和微博数据集上进行了大量实验，结果表明LeRuD在性能上以3.2%至7.7%的优势超越多个最先进的谣言检测模型。同时，由于采用LLMs，LeRuD无需训练数据，因此在少样本或零样本场景下展现出更具前景的谣言检测能力。