Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning

2025/08/10 by Chaoqun Cui, Cui, Chaoqun, Caiyan Jia +1 · 1 citation
Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Misinformation and Its Impacts #Public Relations and Crisis Communication #Social and Information Networks (cs.SI)

paper · pdf · doi:10.48550/arxiv.2508.07205

openalex publication_date 2025/08/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Current rumor detection methods based on propagation structure learning predominately treat rumor detection as a class-balanced classification task on limited labeled data. However, real-world social media data exhibits an imbalanced distribution with a minority of rumors among massive regular posts. To address the data scarcity and imbalance issues, we construct two large-scale conversation datasets from Weibo and Twitter and analyze the domain distributions. We find obvious differences between rumor and non-rumor distributions, with non-rumors mostly in entertainment domains while rumors concentrate in news, indicating the conformity of rumor detection to an anomaly detection paradigm. Correspondingly, we propose the Anomaly Detection framework with Graph Supervised Contrastive Learning (AD-GSCL). It heuristically treats unlabeled data as non-rumors and adapts graph contrastive learning for rumor detection. Extensive experiments demonstrate AD-GSCL's superiority under class-balanced, imbalanced, and few-shot conditions. Our findings provide valuable insights for real-world rumor detection featuring imbalanced data distributions.

Citations

• Fine-grained Video Dubbing Duration Alignment with Segment Supervised Preference Optimization
• Enhancing Rumor Detection Methods with Propagation Structure Infused Language Model
• Propagation Tree Is Not Deep: Adaptive Graph Contrastive Learning Approach for Rumor Detection
• Secrets of RLHF in Large Language Models Part I: PPO
• Unsupervised Cross-Domain Rumor Detection with Contrastive Learning and Cross-Attention
• Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks
• GraphMAE: Self-Supervised Masked Graph Autoencoders
• Detecting Rumours with Latency Guarantees using Massive Streaming Data
• AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators
• Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection
• Graph Contrastive Learning Automated
• Graph Contrastive Learning with Augmentations
• Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter
• Contrastive Multi-View Representation Learning on Graphs
• Language Models are Few-Shot Learners
• GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media
• Interpretable Rumor Detection in Microblogs by Attending to User Interactions
• Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks
• Jointly embedding the local and global relations of heterogeneous graph for rumor detection
• InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
• RoBERTa: A Robustly Optimized BERT Pretraining Approach
• Deep Graph Infomax
• Neural Message Passing for Quantum Chemistry
• Semi-Supervised Classification with Graph Convolutional Networks
• f-GAN: Training Generative Neural Samplers using Variational Divergence\n Minimization
• Adam: A Method for Stochastic Optimization
• Efficient Estimation of Word Representations in Vector Space
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Cited by

• Fine-grained Video Dubbing Duration Alignment with Segment Supervised Preference Optimization
• Graph Representation Learning with Massive Unlabeled Data for Rumor Detection

Related

• A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection
• Graph Representation Learning with Massive Unlabeled Data for Rumor Detection
• Propagation Tree Is Not Deep: Adaptive Graph Contrastive Learning Approach for Rumor Detection
• Rumor Detection with Self-supervised Learning on Texts and Social Graph
• Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks