  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.
