A Unified Model for Unimodal and Multimodal Rumor Detection

Rumor detection aims to determine the truthfulness of a post, no matter it is unimodal plain text or multimodal text and images . However, previous models only considered one of these situations, ignoring the possibility of both occurring simultaneously. Additionally, previous multimodal models often failed to tackle the inconsistency between texts and images, which can produce noise and harm performance. To address the aforementioned issues, we propose a novel unified model for unimodal and multimodal rumor detection, called the Graph Attention Generative Image Network GAGIN , which is integrated with multimodal alignment. The experimental results on two popular datasets demonstrate that GAGIN outperforms the state-of-the-art baselines.