  The rampant spread of fake news has adversely affected society, resulting in
extensive research on curbing its spread. As a notable milestone in large
language models (LLMs), ChatGPT has gained significant attention due to its
exceptional natural language processing capabilities. In this study, we present
a thorough exploration of ChatGPT's proficiency in generating, explaining, and
detecting fake news as follows. Generation -- We employ four prompt methods to
generate fake news samples and prove the high quality of these samples through
both self-assessment and human evaluation. Explanation -- We obtain nine
features to characterize fake news based on ChatGPT's explanations and analyze
the distribution of these factors across multiple public datasets. Detection --
We examine ChatGPT's capacity to identify fake news. We explore its detection
consistency and then propose a reason-aware prompt method to improve its
performance. Although our experiments demonstrate that ChatGPT shows
commendable performance in detecting fake news, there is still room for its
improvement. Consequently, we further probe into the potential extra
information that could bolster its effectiveness in detecting fake news.
