Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning

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.

Authors

• Hongzhan Lin Hong Kong Baptist University
• Pengyao Yi Beijing University of Posts and Telecommunications
• Jing Ma Xidian University
• Haiyun Jiang Tencent AI Lab
• Ziyang Luo Hong Kong Baptist University
• Shuming Shi Tsinghua University
• Ruifang Liu Beijing University of Posts and Telecommunications

Keywords

• APP: Misinformation & Fake News