  The truth is significantly hampered by massive rumors that spread along with
breaking news or popular topics. Since there is sufficient corpus gathered from
the same domain for model training, existing rumor detection algorithms show
promising performance on yesterday's news. However, due to a lack of
substantial training data and prior expert knowledge, they are poor at spotting
rumors concerning unforeseen events, especially those propagated in different
languages (i.e., low-resource regimes). In this paper, we propose a unified
contrastive transfer framework to detect rumors by adapting the features
learned from well-resourced rumor data to that of the low-resourced with only
few-shot annotations. More specifically, we first represent rumor circulated on
social media as an undirected topology for enhancing the interaction of user
opinions, and then train a Multi-scale Graph Convolutional Network via a
unified contrastive paradigm to mine effective clues simultaneously from post
semantics and propagation structure. Our model explicitly breaks the barriers
of the domain and/or language issues, via language alignment and a novel
domain-adaptive contrastive learning mechanism. To well-generalize the
representation learning using a small set of annotated target events, we reveal
that rumor-indicative signal is closely correlated with the uniformity of the
distribution of these events. We design a target-wise contrastive training
mechanism with three event-level data augmentation strategies, capable of
unifying the representations by distinguishing target events. Extensive
experiments conducted on four 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.
