Out-of-distribution Rumor Detection via Test-Time Adaptation
This addresses the challenge of robust rumor detection for social media platforms, but it is incremental as it builds on existing propagation graph methods with test-time adaptation.
The paper tackles the problem of rumor detection under out-of-distribution (OOD) scenarios, where existing methods suffer performance declines due to distribution shifts, and proposes a test-time adaptation method that outperforms state-of-the-art approaches in experiments on real-world social media datasets.
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.