  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.
