FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain
  Fake News Detecction
Ziyi Zhou Xiaoming Zhang Litian Zhang Jiacheng Liu Xi Zhang Chaozhuo Li
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
\url{https://github.com/Accuser907/FineFake}.