Knowledge Enhanced Vision and Language Model for Multi-Modal Fake News Detection

The rapid dissemination of fake news and rumors through the Internet and social media platforms poses significant challenges and raises concerns in the public sphere. Automatic detection of fake news plays a crucial role in mitigating the spread of misinformation. While recent approaches have focused on leveraging neural networks to improve textual and visual representations in multi-modal fake news analysis, they often overlook the potential of incorporating knowledge information to verify facts within news articles. In this paper, we present a vision and language model that incorporates knowledge to enhance multi-modal fake news detection. Our proposed model integrates information from large scale open knowledge graphs to augment its ability to discern the veracity of news content. Unlike previous methods that utilize separate models to extract textual and visual features, we synthesize a unified model capable of extracting both types of features simultaneously. To represent news articles, we introduce a graph structure where nodes encompass entities, relationships extracted from the textual content, and objects depicted in associated images. By utilizing the knowledge graph, we establish meaningful relationships between nodes within the news articles. Experimental evaluations on a real-world multi-modal dataset from Twitter demonstrate significant performance improvement by incorporating knowledge information.