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：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。以往研究表明，由于缺乏标注资源，以少数语言传播的谣言难以被有效检测。此外，未收录于既有新闻报道中的突发事件进一步加剧了数据资源的匮乏。本文提出一种基于提示学习的零样本检测框架，用于检测跨领域或跨语言传播的谣言。具体而言，我们首先将社交媒体上流传的谣言表示为多元传播线程，继而设计分层提示编码机制，以学习提示与谣言数据中与语言无关的上下文表征。为增强域适应能力，我们从传播线程中提取域不变的结构特征，从而融入具有影响力的社区响应的结构位置表征。同时，采用新型虚拟响应增强方法优化模型训练。基于三个真实数据集的广泛实验表明，本模型性能显著优于现有最优方法，且具备早期阶段谣言检测的卓越能力。