Knowledge augmented transformer for adversarial multidomain multiclassification multimodal fake news detection

Abstract

The spread of disinformation and fake news on social platforms has an unfavorable impact on social harmony and stability. The timely and accurate identification of fake news might help restrain the propagation of fake news and mitigate its influence on society. In this paper, we propose a novel multimodal fake news detection framework: the Knowledge Augmented Transformer for adversarial Multidomain multiclassification multimodal Fake news detection framework (KATMF). In contrast to most of the existing studies, which ignore the differences among news articles from different domains in terms of the feature distribution, the KATMF employs a multimodal adversarial multitask learning module to capture these differences. Moreover, because social media news entities generally lack sufficient background knowledge, to enrich news with knowledge information in a homogeneous embedding space, we use the Knowledge Augmented Transformer (KAT) to selectively encode the information of entities from an external knowledge source into the representation of news. We evaluate our approach on a large-scale real-world dataset, and the experimental results demonstrate that our proposed model outperforms state-of-the-art fake news detection methods.