The advent of large language models (LLMs) has revolutionized online content creation, making it much easier to generate high-quality fake news. This misuse threatens the integrity of our digital environment and ethical standards. Therefore, understanding the motivations and mechanisms behind LLM-generated fake news is crucial. In this study, we analyze the creation of fake news from a social psychology perspective and develop a comprehensive LLM-based theoretical framework, LLM-Fake Theory. We introduce a novel pipeline that automates the generation of fake news using LLMs, thereby eliminating the need for manual annotation. Utilizing this pipeline, we create a theoretically informed Machine-generated Fake news dataset, MegaFake, derived from the GossipCop dataset. We conduct comprehensive analyses to evaluate our MegaFake dataset. We believe that our dataset and insights will provide valuable contributions to future research focused on the detection and governance of fake news in the era of LLMs.

翻译：大语言模型（LLMs）的出现彻底改变了在线内容创作，使得生成高质量的假新闻变得容易得多。这种滥用行为威胁着我们数字环境的完整性和道德标准。因此，理解LLM生成假新闻背后的动机和机制至关重要。在本研究中，我们从社会心理学视角分析假新闻的创作，并开发了一个全面的基于LLM的理论框架——LLM-Fake理论。我们引入了一种新颖的流水线，利用LLM自动生成假新闻，从而消除了手动标注的需要。利用该流水线，我们从GossipCop数据集中创建了一个基于理论的机器生成假新闻数据集——MegaFake。我们进行了全面的分析以评估我们的MegaFake数据集。我们相信，我们的数据集和见解将为未来专注于LLM时代假新闻检测与治理的研究提供宝贵的贡献。