Hierarchical Semantic Enhancement Network for Multimodal Fake News Detection

The explosion of multimodal fake news content on social media has sparked widespread concern. Existing multimodal fake news detection methods have made significant contributions to the development of this field, but fail to adequately exploit the potential semantic information of images and ignore the noise embedded in news entities, which severely limits the performance of the models. In this paper, we propose a novel Hierarchical Semantic Enhancement Network (HSEN) for multimodal fake news detection by learning text-related image semantic and precise news high-order knowledge semantic information. Specifically, to complement the image semantic information, HSEN utilizes textual entities as the prompt subject vocabulary and applies reinforcement learning to discover the optimal prompt format for generating image captions specific to the corresponding textual entities, which contain multi-level cross-modal correlation information. Moreover, HSEN extracts visual and textual entities from image and text, and identifies additional visual entities from image captions to extend image semantic knowledge. Based on that, HSEN exploits an adaptive hard attention mechanism to automatically select strongly related news entities and remove irrelevant noise entities to obtain precise high-order knowledge semantic information, while generating attention mask for guiding cross-modal knowledge interaction. Extensive experiments show that our method outperforms state-of-the-art methods.