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.

利用大型语言模型（LLMs）进行社交媒体谣言检测的研究，提出了一种 LLM - 驱动的谣言检测方法（LeRuD），通过设计提示教给 LLMs 在新闻和评论中进行推理，并将整个传播信息分割为传播链以减轻 LLMs 的负担，通过在 Twitter 和微博数据集上的实验，LeRuD 在谣言检测方面表现出色，超过了几种最先进的模型，并且通过应用 LLMs，LeRuD 无需训练数据，在少量或零样本情景中显示出更有前景的谣言检测能力。