Using ALBERT and Multi-modal Circulant Fusion for Fake News Detection

Abstract

Fake news that combines text and images has a better story-telling ability than text-only fake news, making it more deceptive and easier to spread maliciously. Therefore, multi-modal fake news detection has become a new hot topic. There are two main challenges in this task. First, the traditional pre-training model BERT has more parameters and a relatively slow training speed, which limits the level of extracted text features. Second, in the multi-modal form, the fusion process is only a simple splicing of visual and textual features of news, and the obtained multi-modal features are insufficient to express the complementarity between multi-modal data and may have redundant information, potentially leading to biased detection results.In order to solve the above issues, we propose ALB-MCF, using ALBERT and Multi-modal Circulant Fusion(MCF) for fake news detection. ALB-MCF consists of four main modules: a multi-modal feature extractor, a multi-modal feature fusion, a fake news detection and a domain classifier. Specifically, the multi-modal feature extractor innovatively uses a pre-trained ALBERT model to extract text features and a pre-trained VGG19 model to extract visual features. Then, the text features and the visual features are fused into a multi-modal feature representation by MCF, which improves the fusion capability while avoiding an increase in parameters and computational cost. Finally, the multi-modal features are fed into the detector to detect fake news. The role of the domain classifier is mainly to remove event-specific features while retaining shared features between events, thus providing effective detection of emerging events. We have conducted extensive experiments on two real-world datasets. The results demonstrated that our model can handle multi-modal data more effectively, thus improving the accuracy of fake news detection.