GRaMuFeN: Graph-based Multi-modal Fake News Detection in Social Media
Makan Kananian Fatima Badiei S. AmirAli Gh. Ghahramani
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.