Rumor Detection on Social Media with Graph Structured Adversarial Learning
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 1417-1423. https://doi.org/10.24963/ijcai.2020/197
The wide spread of rumors on social media has caused tremendous effects in both the online and offline world. In addition to text information, recent detection methods began to exploit the graph structure in the propagation network. However, without a rigorous design, rumors may evade such graph models using various camouflage strategies by perturbing the structured data. Our focus in this work is to develop a robust graph-based detector to identify rumors on social media from an adversarial perspective. We first build a heterogeneous information network to model the rich information among users, posts, and user comments for detection. We then propose a graph adversarial learning framework, where the attacker tries to dynamically add intentional perturbations on the graph structure to fool the detector, while the detector would learn more distinctive structure features to resist such perturbations. In this way, our model would be enhanced in both robustness and generalization. Experiments on real-world datasets demonstrate that our model achieves better results than the state-of-the-art methods.
Data Mining: Applications
Data Mining: Classification, Semi-Supervised Learning
Data Mining: Mining Graphs, Semi Structured Data, Complex Data