Courtroom-FND: a multi-role fake news detection method based on argument switching-based courtroom debate

作者：Weiqiang Jin, Daizhong Su, Tao Tao, Xiujun Wang, Ningwei Wang, Biao Zhao · 发表于：Journal of King Saud University - Computer and Information Sciences · 年份：2025 · DOI：10.1007/s44443-025-00038-x · 被引用次数：8 · 研究领域：Misinformation and Its Impacts、Hate Speech and Cyberbullying Detection、Spam and Phishing Detection

With the proliferation of the internet and social media, the spread of fake news has become a global issue, posing serious challenges to the research of Fake News Detection (FND) methods. With advancements in Artificial Intelligence (AI), large language models (LLMs) have become increasingly evident across various industries, especially in natural language processing (NLP). LLM-based FND approaches, including Chain-of-Thought (CoT), self-reflection, and in-context learning (ICL) prompting paradigms, has shown promise but still faces challenges in effectively handling complex and nuanced content. For example, CoT paradigm faces error propagation issues, self-reflection methods suffer from the Degeneration-of-Thought (DoT) problem, and ICL paradigm is highly dependent on the quality of the provided context. To address these issues, we propose a multi-role detection method based on courtroom debates. This method involves two attorneys, representing the prosecution and the defense, as well as a judge, simulating a debate process on the authenticity of the news. First, the prosecution attempts to prove that the news is fake, while the defense tries to prove that the news is genuine. The judge evaluates the evidence presented by both sides to reach a conclusion. Next, the prosecution and defense switch roles, with each attempting to argue from the opposite standpoint, and the judge evaluates the arguments again. Finally, the judge synthesizes all arguments to issue a verdict. Exten...