The spread of rumors along with breaking events seriously hinders the truth in the era of social media. Previous studies reveal that due to the lack of annotated resources, rumors presented in minority languages are hard to be detected. Furthermore, the unforeseen breaking events not involved in yesterday's news exacerbate the scarcity of data resources. In this work, we propose a novel zero-shot framework based on prompt learning to detect rumors falling in different domains or presented in different languages. More specifically, we firstly represent rumor circulated on social media as diverse propagation threads, then design a hierarchical prompt encoding mechanism to learn language-agnostic contextual representations for both prompts and rumor data. To further enhance domain adaptation, we model the domain-invariant structural features from the propagation threads, to incorporate structural position representations of influential community response. In addition, a new virtual response augmentation method is used to improve model training. Extensive experiments conducted on three real-world datasets demonstrate that our proposed model achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.

翻译：伴随突发事件的谣言传播严重阻碍了社交媒体时代真相的呈现。已有研究表明，由于缺乏标注资源，少数语种呈现的谣言难以被检测。此外，未在昨日新闻中出现的新突发事件进一步加剧了数据资源的稀缺性。本文提出了一种基于提示学习的新型零样本框架，用于检测不同领域或不同语种呈现的谣言。具体而言，我们首先将社交媒体中传播的谣言表示为多样化的传播线程，然后设计分层提示编码机制，为提示和谣言数据学习语言无关的上下文表征。为增强领域适应性，我们从传播线程中建模领域不变的结构特征，并融合有影响力的社区响应的结构位置表征。此外，我们采用新的虚拟回复增强方法改进模型训练。在三个真实数据集上的大量实验表明，所提模型性能显著优于现有最优方法，并在谣言早期检测阶段展现出卓越能力。