Most of the existing methods only connect multi-modal features, without considering the complex connection between modes. To overcome these limitations, this paper proposed an end-to-end algorithm named matrix factorized bilinear pooling for multi-modal fusion fake news detection. Firstly, the algorithm effectively fused the text and visual features captured by multi-modal feature extractor utilizing matrix factorized bilinear pooling method, and then cooperated with fake news detector to classify posts as fake or not. It added the event classifier to remove the event-specific features. This paper compared with this algorithm and the other algorithms on two multimedia datasets, Twitter and Weibo. Experimental results show that the proposed algorithm can effectively fuse multi-modal data, narrow the heterogeneity difference between modes, so as to improve the accuracy of fake news detection.

[2022-07-05] Accepted Paper /doc/2022.03.0139/v1

[2022-10-05] Printed Article /doc/2022.03.0139/v2