Multimodal Fake News Detection Based on Multi-source Heterogeneous Data Fusion.

In the era of information explosion, people are constantly exposed to a wealth of unreliable information. Nevertheless, societal stability and public trust may be seriously threatened by such unverified or even fabricated false information. In order to discriminate the fake news, several fake news detection approaches have been proposed. However, most of the current multimodal fake news detection methods only rely on news text and images from a single data source. Owing to the scarcity of multimodal information and the limited volume of data, the effectiveness of the majority of these methods is low. As a result, we embrace the approach of amalgamating multi-source heterogeneous data and propose a new network structure FusionNet. In this network structure, we creatively use web crawler technology to expand the data. We achieve double amplification of text and image as opposed to the prior single-mode amplification of text, and by data screening and cleaning, we create multi-source and multimodal datasets for various viewpoints on the same event. We also propose a novel modal fusion approach that leverages the inherent information in news content, integrates an attention mechanism, and explores the shared relevant features between original news and multi-source news. Through empirical results and analysis, our approach is superior to other methods.