EXMULF: An Explainable Multimodal Content-Based Fake News Detection System

In this work, we present an explainable multimodal content-based fake news detection system. It is concerned with the veracity analysis of information based on its textual content and the associated image, together with an Explainable AI (XAI) assistant. To the best of our knowledge, this is the first study that aims to provide a fully explainable multimodal content-based fake news detection system using Latent Dirichlet Allocation (LDA) topic modeling, Vision-and-Language BERT (VilBERT) and Local Interpretable Model-agnostic Explanations (LIME) models. Our experiments on two real-world datasets demonstrate the relevance of learning the connection between two modalities, with an accuracy that exceeds 10 state-of-the-art fake news detection models.