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 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. More specifically, we first represent rumor circulated on social media as an undirected topology, and then train a Multi-scale Graph Convolutional Network via a unified contrastive paradigm. 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 enhance the representation learning from a small set of 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 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.

翻译：摘要：真相因伴随突发新闻或热门话题传播的大量谣言而严重受阻。由于从同一领域收集到充足语料用于模型训练，现有谣言检测算法在昨日新闻上展现出良好性能。然而，由于缺乏训练数据和先验专家知识，这些算法难以有效识别突发事件相关谣言，尤其是以不同语言传播的谣言（即低资源场景）。本文提出一种统一对比迁移框架，通过将高资源谣言数据中习得的特征适配至低资源场景来实现谣言检测。具体而言，我们首先将社交媒体上传播的谣言表示为无向拓扑结构，随后通过统一对比范式训练多尺度图卷积网络。该模型通过语言对齐与新颖的域自适应对比学习机制，显式打破了域和/或语言问题的壁垒。为增强对少量目标事件的特征学习，我们揭示谣言指示信号与这些事件分布的均匀性密切相关。我们设计了包含三种数据增强策略的目标导向对比训练机制，能够通过区分目标事件来统一表示。在从真实微博平台收集的四个低资源数据集上进行的大量实验表明，本框架性能显著优于现有最优方法，并展现出早期谣言检测的卓越能力。