Fake News Quick Detection on Dynamic Heterogeneous Information Networks
This addresses the societal harm of fake news by enabling faster detection, though it is incremental as it builds on existing GNN methods with adaptations for dynamics and efficiency.
The paper tackles the problem of quickly detecting fake news by modeling it as a dynamic heterogeneous information network, proposing a Dynamic Heterogeneous Graph Neural Network (DHGNN) that outperforms other GNN-based models in effectiveness and efficiency on three real-world datasets.
The spread of fake news has caused great harm to society in recent years. So the quick detection of fake news has become an important task. Some current detection methods often model news articles and other related components as a static heterogeneous information network (HIN) and use expensive message-passing algorithms. However, in the real-world, quickly identifying fake news is of great significance and the network may vary over time in terms of dynamic nodes and edges. Therefore, in this paper, we propose a novel Dynamic Heterogeneous Graph Neural Network (DHGNN) for fake news quick detection. More specifically, we first implement BERT and fine-tuned BERT to get a semantic representation of the news article contents and author profiles and convert it into graph data. Then, we construct the heterogeneous news-author graph to reflect contextual information and relationships. Additionally, we adapt ideas from personalized PageRank propagation and dynamic propagation to heterogeneous networks in order to reduce the time complexity of back-propagating through many nodes during training. Experiments on three real-world fake news datasets show that DHGNN can outperform other GNN-based models in terms of both effectiveness and efficiency.