SERN: Stance Extraction and Reasoning Network for Fake News Detection

Fake news brings us panic and misunderstanding against the truth, especially under some unusual circumstances, such as the outbreak of COVID-19. It’s crucial to detect fake news on social media early to avoid further propagation. Previous methods manually label the stances implied in post-reply pairs to aid fake news detection, which costs much time and effort. To solve this problem, a novel Stance Extraction and Reasoning Network (SERN) is proposed to extract the stances implied in post-reply pairs implicitly and integrate the stance representations for fake news detection without manually labeling stances, which saves much time and effort. Besides, the adequate utilization of multimodal content in the news is beneficial for complementing information for unimodal representation and jointly improving decision confidence. Thus, a sentence-guided visual attention mechanism is proposed in the text-image fusion module that leverages text-image content for better fake news detection. Encouraging empirical results on Fakeddit and PHEME demonstrate that our method outperforms the state-of-the-art methods.