  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.
