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 2.4% to 7.6%. 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：在本研究中，我们探索利用大型语言模型（LLMs）进行社交媒体谣言检测。然而，由于LLMs可能难以聚焦于复杂传播信息中的关键线索，且面对海量冗余信息时推理能力受限，使其对包含新闻内容与大量评论的社交媒体完整传播信息进行推理面临挑战。为此，我们提出一种名为LeRuD（基于LLM的谣言检测）的方法，通过设计提示词引导LLMs推理新闻与评论中的关键线索，并将完整传播信息分解为传播链以减轻LLMs的推理负担。在Twitter和微博数据集上的大量实验表明，LeRuD相较于多个现有最优谣言检测模型性能提升2.4%至7.6%。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。