Incorporating Relational Knowledge in Explainable Fake News Detection

Kun Wu, Xu Yuan, Yue Ning

Abstract: The greater public has become aware of the rising prevalence of untrustworthy information in online media. Extensive adaptive detection methods have been proposed for mitigating the adverse effect of fake news. Computational methods for detecting fake news based on the news content have several limitations, such as: 1) Encoding semantics from original texts is limited to the structure of the language in the text, making both bag-of-words and embedding-based features deceptive in the representation of a fake news, and 2) Explainable methods often neglect relational contexts in fake news detection. In this paper, we design a knowledge graph enhanced framework for effectively detecting fake news while providing relational explanation. We first build a credential-based multi-relation knowledge graph by extracting entity relation tuples from our training data and then apply a compositional graph convolutional network to learn the node and relation embeddings accordingly. The pre-trained graph embeddings are then incorporated into a graph convolutional network for fake news detection. Through extensive experiments on three real-world datasets, we demonstrate the proposed knowledge graph enhanced framework has significant improvement in terms of fake news detection as well as structured explainability.