Massive false rumors emerging along with breaking news or trending topics
severely hinder the truth. Existing rumor detection approaches achieve
promising performance on the yesterday's news, since there is enough corpus
collected from the same domain for model training. However, they are poor at
detecting rumors about unforeseen events especially those propagated in
different languages due to the lack of training data and prior knowledge (i.e.,
low-resource regimes). In this paper, we propose an adversarial contrastive
learning framework to detect rumors by adapting the features learned from
well-resourced rumor data to that of the low-resourced. Our model explicitly
overcomes the restriction of domain and/or language usage via language
alignment and a novel supervised contrastive training paradigm. Moreover, we
develop an adversarial augmentation mechanism to further enhance the robustness
of low-resource rumor representation. Extensive experiments conducted on two
low-resource datasets collected from real-world microblog platforms demonstrate
that our framework achieves much better performance than state-of-the-art
methods and exhibits a superior capacity for detecting rumors at early stages.

本文提出了一种基于对抗对比学习框架的谣言检测方法，该方法通过语言对齐和新的监督对比训练范式，将从充分资源的谣言数据中学到的特征适应于低资源的数据；此外，通过对抗增强机制，进一步提高低资源谣言表示的鲁棒性，实验结果表明本文提出的方法可以更好地检测出早期谣言。