* External authors




Joint Stance and Rumor Detection in Hierarchical Heterogeneous Graph

Chen li*

Hao Peng*

Jianxin Li*

Lichao Sun*

Lingjuan Lyu

Lihong Wang*

Philip Yu*

Lifang He*

* External authors

IEEE Transactions on Neural Networks and Learning Systems



Recently, large volumes of false or unverified information (e.g., fake news and rumors) appear frequently in emerging social media, which are often discussed on a large scale and widely disseminated, causing bad consequences. Many studies on rumor detection indicate that the stance distribution of posts is closely related to the rumor veracity. However, these two tasks are generally considered separately or just using a shared encoder/layer via multitask learning, without exploring the more profound correlation between them. In particular, the performance of existing methods relies heavily on the quality of hand-crafted features and the quantity of labeled data, which is not conducive to early rumor detection and few-shot detection. In this article, we construct a hierarchical heterogeneous graph by associating posts containing the same high-frequency words to facilitate the feature cross-topic propagation and jointly formulate stance and rumor detection as multistage classification tasks. To realize the updating of node embeddings jointly driven by stance and rumor detection, we propose a multigraph neural network framework, which can more flexibly capture the attribute and structure information of the context. Experiments on real datasets collected from Twitter and Reddit show that our method outperforms state-of-the-art by a large margin on both stance and rumor detection. And the experimental results also show that our method has better interpretability and requires less labeled data.

Related Publications

Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?

NeurIPS, 2023
Xiaoxiao Sun*, Nidham Gazagnadou, Vivek Sharma, Lingjuan Lyu, Hongdong Li*, Liang Zheng*

Hand-crafted image quality metrics, such as PSNR and SSIM, are commonly used to evaluate model privacy risk under reconstruction attacks. Under these metrics, reconstructed images that are determined to resemble the original one generally indicate more privacy leakage. Image…

UltraRE: Enhancing RecEraser for Recommendation Unlearning via Error Decomposition

NeurIPS, 2023
Yuyuan Li*, Chaochao Chen*, Yizhao Zhang*, Weiming Liu*, Lingjuan Lyu, Xiaolin Zheng*, Dan Meng*, Jun Wang*

With growing concerns regarding privacy in machine learning models, regulations have committed to granting individuals the right to be forgotten while mandating companies to develop non-discriminatory machine learning systems, thereby fueling the study of the machine unlearn…

Towards Personalized Federated Learning via Heterogeneous Model Reassembly

NeurIPS, 2023
Jiaqi Wang*, Xingyi Yang*, Suhan Cui*, Liwei Che*, Lingjuan Lyu, Dongkuan Xu*, Fenglong Ma*

This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures. To track this problem, we propose a novel framework called pFedHR, which leverages heterogeneo…

  • HOME
  • Publications
  • Joint Stance and Rumor Detection in Hierarchical Heterogeneous Graph


Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.