CSI: A Hybrid Deep Model for Fake News

In the recent political climate, the topic of fake news has drawn attention both from the public and the academic communities. Such misinformation has been cited to have a strong impact on public opinion, presenting the opportunity for malicious manipulation. Detecting fake news is an important, yet challenging problem since it is often difficult for humans to distinguish misinformation. However, there have been three generally agreed upon characteristics of fake news: the text, the response received, and the source users promoting it. Existing work has largely focused on tailoring solutions to a particular characteristic, but the complexity of the fake news epidemic limited their success and generality. In this work, we propose a model that combines all three characteristics for a more accurate and automated prediction. Specifically, we incorporate the behavior of both parties, users and articles, and the group behavior of users who propagate fake news. Motivated by the three characteristics, we propose a model called CSI, which is composed of three modules: Capture, Score, and Integrate. The first module uses a Recurrent Neural Network (RNN) to capture the temporal pattern of user activity that occurred with a given article, and the second captures the behavior of users over time. The two are then integrated with the third module to classify an article as fake or not. Through experimental analysis on real-world data, we demonstrate that CSI achieves higher accuracy than existing models. Further, we show that each module captures relevant behavioral information both on users and articles with respect to the propagation of fake news.