Establishing a robust rumor detection model is vital in safeguarding the veracity of information on social media platforms. However, existing approaches to stopping rumor from spreading rely on abundant and clean training data, which is rarely available in real-world scenarios. In this work, we aim to develop a trustworthy rumor detection model that can handle inadequate and noisy labeled data. Our work addresses robust rumor detection, including classic and early detection, as well as five types of robustness issues: noisy and incomplete propagation, label scarcity and noise, and user disappearance. We propose a novel method, Robustness-Enhanced Rumor Detection (RERD), which mainly leverages the information propagation graphs of source tweets, along with user profiles and retweeting knowledge, for model learning. The novelty of RERD is four-fold. First, we jointly exploit the propagation structures of non-text and text retweets to learn the representation of a source tweet. Second, we simultaneously utilize the top-down and bottom-up information flows with relational propagations for graph representation learning. Third, to have effective early and robust detection, we implement contrastive learning on graphs with early and complete views of information propagation so that small snapshots can foresee their future shapes. Last, we use curriculum pseudo-labeling to mitigate the impact of label scarcity and noisy labels, and to correct representations learned from corrupted data. Experimental results on three benchmark datasets demonstrate that RERD consistently outperforms competitors in classic, early, and robust rumor detection scenarios. To the best of our knowledge, we are the first to simultaneously cope with early and five robust detections of rumors.

建立强大的谣言检测模型对于维护社交媒体平台上信息的真实性至关重要。然而，现有的阻止谣言传播的方法依赖于丰富且干净的训练数据，而这在现实场景中很少可用。在这项工作中，我们的目标是开发一种值得信赖的谣言检测模型，可以处理不充分且有噪声的标记数据。我们的工作解决了稳健的谣言检测，包括经典和早期检测，以及五种稳健性问题：噪声和不完整传播、标签稀缺和噪声以及用户消失。我们提出了一种新颖的方法，即鲁棒性增强的谣言检测（RERD），该方法主要利用源推文的信息传播图以及用户个人资料和转发知识来进行模型学习。 RERD 的新颖性有四个方面。首先，我们共同利用非文本和文本转发的传播结构来学习源推文的表示。其次，我们同时利用自上而下和自下而上的信息流与关系传播进行图表示学习。第三，为了进行有效的早期和鲁棒检测，我们通过信息传播的早期和完整视图对图进行对比学习，以便小快照可以预见其未来的形状。最后，我们使用课程伪标签来减轻标签稀缺和噪声标签的影响，并纠正从损坏的数据中学到的表示。三个基准数据集的实验结果表明，RERD 在经典、早期和稳健的谣言检测场景中始终优于竞争对手。据我们所知，我们是第一个同时应对早期和五次强有力的谣言检测的人。