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:在社交媒体时代,谣言传播和突发事件损害了真相。之前的研究显示,由于缺乏标注资源,少数语言中存在的谣言很难被检测到。此外,昨天的新闻中没有涉及的意外突发事件加剧了数据资源的匮乏。在本文中,我们提出了一种基于Prompt学习的零样本框架,以检测不同领域或不同语言中出现的谣言。具体而言,我们首先将社交媒体上传播的谣言表示为不同的传播线程,然后设计了一种分层Prompt编码机制,用于学习提示和谣言数据的语言不可知的上下文表示。为了进一步增强领域自适应性,我们从传播线程中模拟领域不变的结构特征,以结合影响社区响应的结构位置表示。另外,我们使用新的虚拟响应增强方法来改善模型训练。在三个真实数据集上进行的广泛实验表明,我们提出的模型比现有方法表现更好,并具有检测早期谣言的卓越能力。