Existing benchmarks for fake news detection have significantly contributed to
the advancement of models in assessing the authenticity of news content.
However, these benchmarks typically focus solely on news pertaining to a single
semantic topic or originating from a single platform, thereby failing to
capture the diversity of multi-domain news in real scenarios. In order to
understand fake news across various domains, the external knowledge and
fine-grained annotations are indispensable to provide precise evidence and
uncover the diverse underlying strategies for fabrication, which are also
ignored by existing benchmarks. To address this gap, we introduce a novel
multi-domain knowledge-enhanced benchmark with fine-grained annotations, named
\textbf{FineFake}. FineFake encompasses 16,909 data samples spanning six
semantic topics and eight platforms. Each news item is enriched with
multi-modal content, potential social context, semi-manually verified common
knowledge, and fine-grained annotations that surpass conventional binary
labels. Furthermore, we formulate three challenging tasks based on FineFake and
propose a knowledge-enhanced domain adaptation network. Extensive experiments
are conducted on FineFake under various scenarios, providing accurate and
reliable benchmarks for future endeavors. The entire FineFake project is
publicly accessible as an open-source repository at
https://github.com/Accuser907/FineFake.

为了理解不同领域的虚假信息，我们引入了一个新颖的多领域知识增强基准，名为 FineFake，它包含 16909 个数据样本，涵盖六个语义主题和八个平台，并提供多模态内容，潜在社会背景，半手动验证的常见知识和超越传统二进制标签的细粒度注释。基于 FineFake，我们还提出了三个具有挑战性的任务，并提出了一个知识增强的领域自适应网络，在各种情况下对 FineFake 进行了广泛的实验证明，为未来的研究提供了准确可靠的基准。整个 FineFake 项目可以在 https://github.com/Accuser907/FineFake 上作为开源资源进行访问。