Hierarchical Multi-modal Contextual Attention Network for Fake News Detection

Nowadays, detecting fake news on social media platforms has become a top priority since the widespread dissemination of fake news may mislead readers and have negative effects. To date, many algorithms have been proposed to facilitate the detection of fake news from the hand-crafted feature extraction methods to deep learning approaches. However, these methods may suffer from the following limitations: (1) fail to utilize the multi-modal context information and extract high-order complementary information for each news to enhance the detection of fake news; (2) largely ignore the full hierarchical semantics of textual content to assist in learning a better news representation. To overcome these limitations, this paper proposes a novel hierarchical multi-modal contextual attention network (HMCAN) for fake news detection by jointly modeling the multi-modal context information and the hierarchical semantics of text in a unified deep model. Specifically, we employ BERT and ResNet to learn better representations for text and images, respectively. Then, we feed the obtained representations of images and text into a multi-modal contextual attention network to fuse both inter-modality and intra-modality relationships. Finally, we design a hierarchical encoding network to capture the rich hierarchical semantics for fake news detection. Extensive experiments on three public real datasets demonstrate that our proposed HMCAN achieves state-of-the-art performance.