Abstract
On the Internet, fake news exists in various domain (e.g., education, health). Since news in different domains has different features, researchers have begun to use single domain label for fake news detection recently. 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 one 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. Finally, the discriminator module uses the total feature representation to discriminate 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 dataset which has no domain label.
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Liang, C., Zhang, Y., Li, X., Zhang, J., Yu, Y. (2022). FuDFEND: Fuzzy-Domain for Multi-domain Fake News Detection. In: Lu, W., Huang, S., Hong, Y., Zhou, X. (eds) Natural Language Processing and Chinese Computing. NLPCC 2022. Lecture Notes in Computer Science(), vol 13552. Springer, Cham. https://doi.org/10.1007/978-3-031-17189-5_4
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