Topic-Aware Fake News Detection Based on Heterogeneous Graph

• Published in: IEEE access 2023, Vol.11, p.103743-103752
• Main Authors: Sun, Lijuan, Wang, Hongbin
• Format: Article
• Language: English
• Subjects: double-layer heterogeneous graph attention network; Fake news; Fake news detection; Feature extraction; Graphs; heterogeneous graph; Internet; News; Semantics; Social networking (online); Task analysis; topic-aware; Writing
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3318483

In recent years, fake news has had a bad impact on individuals and society, which has aroused widespread concern about fake news detection. The existing heterogeneous graph-based fake news detection model (CompareNet) mainly focuses on the semantic consistency analysis between news content and external knowledge, which out-performs traditional content detection models in terms of its efficiency and scalability. However, we found that the framework ignores the fact that the node content of heterogeneous graphs is mostly in the form of short text, and such methods often have difficulty in extracting effective features due to the sparsity problem of short text data. In addition, previous studies have not considered the structural relationship between different writing styles of fake news. Aiming at the above problems this paper proposes a topic-aware fake news detection (FND) method based on heterogeneous graphs, the model investigates the effect of news topics on fake news detection and enhances the discriminative ability of fake news detection. Our model introduces semantically enhanced topic node information in the fake news detector, which fully utilizes three types of information: external knowledge (Wikipedia), news content, and news topics. Therefore, it can better enhance the fake news detection performance.