Rumor Detection with Adaptive Data Augmentation and Adversarial Training

Abstract

Rumors are widely spread on social media, which has a negative impact on social stability. To address this problem, many rumor detection methods have been proposed. However, most existing methods overlook the potential impact of noise and adversarial attacks on their detection performance, which could compromise their effectiveness when applied in an unknown environment. To overcome these challenges and improve the framework robustness to noise and adversarial attacks, we propose a novel rumor detection framework with Adaptive Data Augmentation and Adversarial Training, named ADAAT. Our framework utilizes the adaptive data augmentation module to calculate the importance of edges and features and adaptively modify the less important among them with a greater probability. In addition, it contains a hard sample generation module which generates adversarial representations through adversarial training. These adversarial representations are treated as hard samples, which are utilized in contrastive learning to learn essential features, thereby improving the robustness of the framework. Our framework proves superiority in rumor detection tasks, increasing the accuracy by an average of 3.6%, 4.5% and 2.5% over the state-of-the-art methods on Twitter15, Twitter16 and PHEME, respectively. When the ADAAT framework is applied to attacked test data, the detection accuracy decreases by only 1.3%, 1.4%, and 1.2%. This paper appears in the AI & Society Track.