5 papers
Avoiding Over-smoothing in Social Media Rumor Detection with Pre-trained Propagation Tree Transformer
Chaoqun Cui, Caiyan Jia
Deep learning techniques for rumor detection typically utilize Graph Neural Networks (GNNs) to analyze post relations. These methods, however, falter due to over-smoothing issues w…
Enhancing Rumor Detection Methods with Propagation Structure Infused Language Model
Chaoqun Cui, Siyuan Li, Kunkun Ma +1
Pretrained Language Models (PLMs) have excelled in various Natural Language Processing tasks, benefiting from large-scale pretraining and self-attention mechanism's ability to capt…
Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning
Chaoqun Cui, Caiyan Jia
Current rumor detection methods based on propagation structure learning predominately treat rumor detection as a class-balanced classification task on limited labeled data. However…
Propagation Tree Is Not Deep: Adaptive Graph Contrastive Learning Approach for Rumor Detection
Chaoqun Cui, Caiyan Jia
Rumor detection on social media has become increasingly important. Most existing graph-based models presume rumor propagation trees (RPTs) have deep structures and learn sequential…
Graph Representation Learning with Massive Unlabeled Data for Rumor Detection
Chaoqun Cui, Caiyan Jia
With the development of social media, rumors spread quickly, cause great harm to society and economy. Thereby, many effective rumor detection methods have been developed, among whi…