The proliferation of fake news has emerged as a critical issue in recent
years, requiring significant efforts to detect it. However, the existing fake
news detection datasets are sourced from human journalists, which are likely to
have inherent bias limitations due to the highly subjective nature of this
task. In this paper, we revisit the existing fake news dataset verified by
human journalists with augmented fact-checking by large language models
(ChatGPT), and we name the augmented fake news dataset ChatGPT-FC. We
quantitatively analyze the distinctions and resemblances between human
journalists and LLM in assessing news subject credibility, news creator
credibility, time-sensitive, and political framing. Our findings highlight
LLM's potential to serve as a preliminary screening method, offering a
promising avenue to mitigate the inherent biases of human journalists and
enhance fake news detection.

通过扩充大语言模型 (ChatGPT) 的事实核查，重新审视了由人类记者验证的现有虚假新闻数据集，并将扩充的虚假新闻数据集命名为 ChatGPT-FC。我们在评估新闻主题可信度、新闻创作者可信度、时态敏感度和政治框架方面定量分析了人类记者和 LLM 之间的差异和相似之处。我们的研究结果强调了 LLM 作为初步筛选方法的潜力，为减轻人类记者的固有偏见并增强虚假新闻检测提供了有希望的途径。