An interpretable wide and deep model for online disinformation detection

Abstract

The advent of the internet has facilitated the wide spread of online disinformation and thus poses severe threats to the trustworthiness of cyberspace. Two types of methods are proposed to detect online disinformation: traditional machine learning-based and deep learning-based, where the former is limited due to the shallow representation and the latter is hindered by its lack of interpretability. In this study, we develop a novel model named interpretable wide and deep model for text (IWDMT) for disinformation detection which incorporates the interpretability benefits of traditional machine learning and the representation advantages of deep learning. Furthermore, we advance the interpretability of existing models by utilizing neural topic models to capture topical semantic representations and the attention mechanism to extract sequential syntactic representations. The proposed IWDMT is a mixture of a generative model and a discriminative model, and we devise a novel learning algorithm for it. Experiments on deceptive reviews and fraudulent emails demonstrated the proposed IWDMT not only outperformed baselines but was also able to provide a rich set of angles of interpretation for management insights. The higher accuracy and improved interpretability in detecting online disinformation will benefit four stakeholder groups: internet users, managers, researchers, and the government.