The paper introduces a novel approach to address the challenges of fake news detection by proposing the "Rumor Large Language Models" (RumorLLM), a large language model fine-tuned with rumor writing styles and content. The key contributions include the development of RumorLLM and a data-augmentation method for small categories, effectively mitigating the issue of category imbalance in real-world fake-news datasets. Experimental results on the BuzzFeed and PolitiFact datasets demonstrate the superiority of the proposed model over baseline methods, particularly in F1 score and AUC-ROC. The model's robust performance highlights its effectiveness in handling imbalanced datasets and provides a promising solution to the pressing issue of false-information proliferation. The paper also discusses the limitations and future research directions, emphasizing the need for continuous model evaluation, enhanced interpretability, and ethical considerations.The paper introduces a novel approach to address the challenges of fake news detection by proposing the "Rumor Large Language Models" (RumorLLM), a large language model fine-tuned with rumor writing styles and content. The key contributions include the development of RumorLLM and a data-augmentation method for small categories, effectively mitigating the issue of category imbalance in real-world fake-news datasets. Experimental results on the BuzzFeed and PolitiFact datasets demonstrate the superiority of the proposed model over baseline methods, particularly in F1 score and AUC-ROC. The model's robust performance highlights its effectiveness in handling imbalanced datasets and provides a promising solution to the pressing issue of false-information proliferation. The paper also discusses the limitations and future research directions, emphasizing the need for continuous model evaluation, enhanced interpretability, and ethical considerations.