Socially Aware Multimodal Deep Neural Networks for Fake News Classification

The importance of fake news detection and classification on Online Social Networks (OSN) has recently increased and drawn attention. Training machine learning models for this task requires different types of attributes or modalities for the target OSN. Existing methods mainly rely on social media text, which carries rich semantic information and can roughly explain the discrepancy between normal and multiple fake news types. However, the structural characteristics of OSNs are overlooked. This paper aims to exploit such structural characteristics and further boost the fake news classification performance on OSN. Using deep neural networks, we build a novel multimodal classifier that incorporates relaying features, textual features, and network feature concatenated with each other in a late fusion manner. Experimental results on benchmark datasets demonstrate that our socially aware architecture outperforms existing models on fake news classification.