Due to the rapid spread of rumors on social media, rumor detection has become an extremely important challenge. Existing methods for rumor detection have achieved good performance, as they have collected enough corpus from the same data distribution for model training. However, significant distribution shifts between the training data and real-world test data occur due to differences in news topics, social media platforms, languages and the variance in propagation scale caused by news popularity. This leads to a substantial decline in the performance of these existing methods in Out-Of-Distribution (OOD) situations. To address this problem, we propose a simple and efficient method named Test-time Adaptation for Rumor Detection under distribution shifts (TARD). This method models the propagation of news in the form of a propagation graph, and builds propagation graph test-time adaptation framework, enhancing the model's adaptability and robustness when facing OOD problems. Extensive experiments conducted on two group datasets collected from real-world social platforms demonstrate that our framework outperforms the state-of-the-art methods in performance.

翻译：由于社交媒体上谣言的快速传播，谣言检测已成为一项极为重要的挑战。现有的谣言检测方法通过从相同数据分布中收集足够语料进行模型训练，已取得良好性能。然而，新闻话题、社交媒体平台、语言的差异，以及新闻热度导致的传播规模变化，使得训练数据与真实世界测试数据之间存在显著分布偏移。这导致这些现有方法在分布外（OOD）场景下性能大幅下降。针对这一问题，我们提出一种简单高效的方法，名为基于分布偏移下测试时适应的谣言检测方法（TARD）。该方法以传播图形式对新闻传播过程进行建模，并构建传播图测试时适应框架，增强了模型面对分布外问题时的适应性与鲁棒性。在从真实社交平台收集的两组数据集上进行的大量实验表明，我们的框架在性能上优于现有最先进方法。