Adversary-Aware Rumor Detection

Abstract

While social media becomes a primary source of news now, it also becomes more challenging for people to distinguish the rumors and non-rumors, which attracts malicious manipulation and may lead to public health harm or economic loss. Consequently, many rumor detection models have been proposed to automatically detect the rumors based on the contents and propagation path. However, most previous works are not aware of malicious attacks, e.g., framing. Therefore, we propose a novel rumor detection framework, Adversary-Aware Rumor Detection including Weighted-Edge Transformer-Graph Network and Position-aware Adversarial Response Generator, to improve the vulnerability of detection models. To the best of our knowledge, this is the first work that can generate the adversarial response with the consideration of the response position. Experimental results show that our model achieves the state-of-the-art on various rumor detection tasks by the proposed Weighted-Edge Transformer-Graph Network and can maintain the performance under the adversarial response attack after the adversarial learning by Position-aware Adversarial Response Generator.