MDG: Fusion learning of the maximal diffusion, deep propagation and global structure features of fake news

Fake news detection has attracted much attention in the past five years. Some early studies use news content to detect fake news. As content feature-based methods lack generalizability for fake news detection, researchers explored methods that use social context features of news. However, existing studies only use single-feature (either propagation or structure) detection, but none uses diffusion group information (e.g., the group with the most user responses in the news events) in detecting fake news. Thus, this research develops a fake news detection framework named MDG, which learns and integrates the maximal diffusion feature with the deep propagation and global structure features. To validate the performance of our framework, two sets of experiments are designed on three publicly available datasets, i.e., Twitter15, Twitter16, and Weibo. Experimental results show that our framework outperforms the state-of-the-art methods in accuracy and F1-macro performance. Moreover, our framework is especially suitable for multi-lingual fake news detection.