HG-SL: Jointly Learning of Global and Local User Spreading Behavior for Fake News Early Detection
Keywords:APP: Misinformation & Fake News, DMKM: Applications, DMKM: Graph Mining, Social Network Analysis & Community Mining, DMKM: Web Personalization & User Modeling, APP: Social Networks, KRR: Applications, ML: Graph-based Machine Learning
AbstractRecently, fake news forgery technology has become more and more sophisticated, and even the profiles of participants may be faked, which challenges the robustness and effectiveness of traditional detection methods involving text or user identity. Most propagation-only approaches mainly rely on neural networks to learn the diffusion pattern of individual news, which is insufficient to describe the differences in news spread ability, and also ignores the valuable global connections of news and users, limiting the performance of detection. Therefore, we propose a joint learning model named HG-SL, which is blind to news content and user identities, but capable of catching the differences between true and fake news in the early stages of propagation through global and local user spreading behavior. Specifically, we innovatively design a Hypergraph-based Global interaction learning module to capture the global preferences of users from their co-spreading relationships, and introduce node centrality encoding to complement user influence in hypergraph learning. Moreover, the designed Self-attention-based Local context learning module first introduce spread status to highlight the propagation ability of news and users, thus providing additional signals for verifying news authenticity. Experiments on real-world datasets indicate that our HG-SL, which solely relies on user behavior, outperforms SOTA baselines utilizing multidimensional features in both fake news detection and early detection task.
How to Cite
Sun, L., Rao, Y., Lan, Y., Xia, B., & Li, Y. (2023). HG-SL: Jointly Learning of Global and Local User Spreading Behavior for Fake News Early Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 5248-5256. https://doi.org/10.1609/aaai.v37i4.25655
AAAI Technical Track on Domain(s) of Application