  On the Internet, fake news exists in various domain (e.g., education,
health). Since news in different domains has different features, researchers
have be-gun to use single domain label for fake news detection recently. This
emerg-ing field is called multi-domain fake news detection (MFND). Existing
works show that using single domain label can improve the accuracy of fake news
detection model. However, there are two problems in previous works. Firstly,
they ignore that a piece of news may have features from different domains. The
single domain label focuses only on the features of the news on particu-lar
domain. This may reduce the performance of the model. Secondly, their model
cannot transfer the domain knowledge to the other dataset without domain label.
In this paper, we propose a novel model, FuDFEND, which solves the limitations
above by introducing the fuzzy inference mechanism. Specifically, FuDFEND
utilizes a neural network to fit the fuzzy inference process which constructs a
fuzzy domain label for each news item. Then, the feature extraction module uses
the fuzzy domain label to extract the multi-domain features of the news and
obtain the total feature representation. Fi-nally, the discriminator module
uses the total feature representation to dis-criminate whether the news item is
fake news. The results on the Weibo21 show that our model works better than the
model using only single domain label. In addition, our model transfers domain
knowledge better to Thu da-taset which has no domain label.
