Contrastive Multi-Knowledge Graph Learning for Fake News Detection

The widespread dissemination of fake news on social media platforms has significantly disrupted people's daily lives, causing substantial inconvenience and loss. Although fake news detection has captivated great research effort, most of the existing works either overlook the complementary ground knowledge, formulated in individual knowledge graphs (KGs) or ignore multiple aspects of knowledge in different knowledge graphs. In this paper, we propose a novel contrastive learning framework for investigating the complementary knowledge in different knowledge graphs, which achieves superior fake news detection performance. We first model three components within news articles (i.e., news content, entities, and topics) as a heterogeneous graph (HG). Subsequently, we leverage the valuable factual evidence among real-world entities, which is enclosed in two open-source knowledge graphs (Wikidata and DBpedia), and augment the heterogeneous graph into three augmented graphs. We then fuse knowledge-enhanced high-level news representations from each augmented graph and extract a unified news representation through our proposed contrastive multi-knowledge graph learning framework, which is ultimately utilized to identify fake news. We conduct extensive experiments on four public fake news datasets and demonstrate the superiority of our method over nine state-of-the-art baselines regarding five widely-used evaluation metrics (i.e., accuracy, precision, recall, F1-score, and ROC).