Abstract: Rumor detection has been an interdisciplinary research topic since the mid-20th century.The rapid rise of social-media platforms such as Weibo and Twitter has kept the task in the spotlight,and the surge of rumors during the 2016 U.S.presidential election brought it to wider public attention.Breakthroughs in LLMs have dramatically advanced natural-language understanding and generation,catalyzing profound changes in the field of rumor detection.This paper presents a systematic survey of the latest studies on rumor generation and detection in the LLM era.It firstly revisits the concept of social-media rumors and summarizes widely used benchmark datasets,tracing the evolution of detection frameworks from traditional machine learning to deep learning and graph neural networks.It then analyzes in depth the four core roles that LLMs play in rumor detection,parameter fine-tu-ning,zero/few-shot prompting,knowledge augmentation and multimodal fusion.In addition,it catalogs datasets containing LLM-generated rumors and examines emerging detection techniques for AI-generated content,such as watermarking,linguistic fingerprints,and semantic-entropy-based methods.This paper concludes by outlining future research directions and the key challenges that remain.

Key words: Rumor, Large Language Models(LLMs), Deep learning, Detection techniques