Massive rumors usually appear along with breaking news or trending topics, seriously hindering the truth. Existing rumor detection methods are mostly focused on the same domain, and thus have poor performance in cross-domain scenarios due to domain shift. In this work, we propose an end-to-end instance-wise and prototype-wise contrastive learning model with a cross-attention mechanism for cross-domain rumor detection. The model not only performs cross-domain feature alignment but also enforces target samples to align with the corresponding prototypes of a given source domain. Since target labels in a target domain are unavailable, we use a clustering-based approach with carefully initialized centers by a batch of source domain samples to produce pseudo labels. Moreover, we use a cross-attention mechanism on a pair of source data and target data with the same labels to learn domain-invariant representations. Because the samples in a domain pair tend to express similar semantic patterns, especially on the people's attitudes (e.g., supporting or denying) towards the same category of rumors, the discrepancy between a pair of the source domain and target domain will be decreased. We conduct experiments on four groups of cross-domain datasets and show that our proposed model achieves state-of-the-art performance.

翻译：大规模谣言通常伴随突发新闻或热门话题出现，严重阻碍真相传播。现有谣言检测方法多聚焦于同域场景，由于域迁移导致其在跨域场景中性能不佳。本研究提出一种端到端的实例级与原型级对比学习模型，结合交叉注意力机制实现跨域谣言检测。该模型不仅执行跨域特征对齐，还强制目标样本与给定源域的相应原型对齐。由于目标域中缺乏标签信息，我们采用基于聚类的策略，利用源域样本的批量数据精心初始化聚类中心，从而生成伪标签。此外，我们利用交叉注意力机制处理具有相同标签的源域与目标域数据对，以学习域不变表征。由于域对中的样本倾向于表达相似语义模式，尤其是对同类谣言的态度（如支持或否认），源域与目标域之间的差异将得以缩小。我们在四组跨域数据集上开展实验，结果表明所提模型达到最优性能。