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Recently, fake news with text and images have achieved more effectivediffusion than text-only fake news, raising a severe issue of multimodal fakenews detection. Current studies on this issue have made significantcontributions to developing multimodal models, but they are defective inmodeling the multimodal content sufficiently. Most of them only preliminarilymodel the basic semantics of the images as a supplement to the text, whichlimits their performance on detection. In this paper, we find three valuabletext-image correlations in multimodal fake news: entity inconsistency, mutualenhancement, and text complementation. To effectively capture these multimodalclues, we innovatively extract visual entities (such as celebrities andlandmarks) to understand the news-related high-level semantics of images, andthen model the multimodal entity inconsistency and mutual enhancement with thehelp of visual entities. Moreover, we extract the embedded text in images asthe complementation of the original text. All things considered, we propose anovel entity-enhanced multimodal fusion framework, which simultaneously modelsthree cross-modal correlations to detect diverse multimodal fake news.Extensive experiments demonstrate the superiority of our model compared to thestate of the art.

Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues