FACADE: Fake Articles Classification and Decision Explanation.

The daily use of social networks and the resulting dissemination of disinformation over those media have greatly contributed to the rise of the fake news phenomenon as a global problem. Several manual and automatic approaches are currently in place to try to tackle and defuse this issue, which is becoming nearly uncontrollable. In this paper, we propose Facade, a fake news detection system that aims to provide a complete solution for classifying news articles and explain the motivation behind every prediction. The system is designed with a cascading architecture composed of two classification pipelines dealing with either low-level or high-level descriptors, with the overall goal of achieving a consistent confidence score on each outcome. In addition, the system is equipped with an explainable user interface through which fact-checkers and content managers can visualise in detail the features leading to a certain prediction and have the possibility for manual cross-checking.