GRaMuFeN: Graph-based Multi-modal Fake News Detection in Social Media
This addresses the problem of false information spread on platforms like Twitter and Weibo for users and authorities, but it is incremental as it builds on existing multi-modal methods with specific architectural improvements.
The paper tackles fake news detection in social media by proposing GraMuFeN, a model that combines graph-based text analysis with image encoding using GCN and CNN, achieving a 10% increase in micro F1-Score over state-of-the-art models on benchmark datasets.
The proliferation of social media platforms such as Twitter, Instagram, and Weibo has significantly enhanced the dissemination of false information. This phenomenon grants both individuals and governmental entities the ability to shape public opinions, highlighting the need for deploying effective detection methods. In this paper, we propose GraMuFeN, a model designed to detect fake content by analyzing both the textual and image content of news. GraMuFeN comprises two primary components: a text encoder and an image encoder. For textual analysis, GraMuFeN treats each text as a graph and employs a Graph Convolutional Neural Network (GCN) as the text encoder. Additionally, the pre-trained ResNet-152, as a Convolutional Neural Network (CNN), has been utilized as the image encoder. By integrating the outputs from these two encoders and implementing a contrastive similarity loss function, GraMuFeN achieves remarkable results. Extensive evaluations conducted on two publicly available benchmark datasets for social media news indicate a 10 % increase in micro F1-Score, signifying improvement over existing state-of-the-art models. These findings underscore the effectiveness of combining GCN and CNN models for detecting fake news in multi-modal data, all while minimizing the additional computational burden imposed by model parameters.