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.

翻译：真相往往受到伴随突发新闻或热门话题传播的海量谣言的严重干扰。由于同一领域积累的语料库足以支撑模型训练，现有谣言检测算法在应对“昨日新闻”时表现出色。然而，因缺乏大量训练数据和先验专家知识，这些算法难以识别涉及突发事件（尤其是以不同语言传播的低资源场景）的谣言。本文提出一种统一的对比迁移框架，通过将来自高资源谣言数据的特征适配到仅拥有少量标注样本的低资源场景，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图以增强用户观点的交互性，然后通过统一对比范式训练多尺度图卷积网络，从帖子语义与传播结构中同时挖掘有效线索。模型通过语言对齐及新型领域自适应对比学习机制，明确突破了领域和语言障碍。为利用少量标注目标事件实现表征学习的良好泛化，我们揭示了谣言指示信号与目标事件分布均匀性之间的密切关联，并设计了针对目标事件的对比训练机制，结合三种事件级数据增强策略，通过区分目标事件实现表征的统一。在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先进方法，并在谣言早期检测阶段展现出了卓越能力。