  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.
