Sensing the diversity of rumors: Rumor detection with hierarchical prototype contrastive learning

The proliferation of rumors on social networks poses a serious threat to cybersecurity, justice and public trust, increasing the urgent need for rumor detection. Existing detection methods typically treat all rumors as a single homogeneous category, neglecting the diverse semantic hierarchies within rumors. Rumors pervade various domains, each with its distinct characteristics. These methods tend to lag in expressiveness when confronted with real-world scenarios involving multiple semantic levels. Furthermore, the diversity of rumors also complicates the collection of datasets, and inevitably introduces noisy data, which hinders the correctness of the learned representations. To address these challenges, we propose a rumor detection framework with Hierarchical Prototype Contrastive Learning (HPCL). In this framework, we construct a set of dynamically updated hierarchical prototypes through contrastive learning to encourage capturing the hierarchical semantic structure within rumors. Additionally, we design a difficulty metric function based on the distance between instances and prototypes, and introduce curriculum learning to mitigate the adverse effects of noisy data. Experiments on four public datasets demonstrate that our approach achieves state-of-the-art performance. Our code is publicly released at https://github.com/Coder-HenryZa/HPCL.