Abstract

Countering the fake news phenomenon has become one of the most important challenges for democratic societies, governments and non-profit organizations, as well as for the researchers coming from several domains. This is not a local problem and demands a holistic approach to analyzing heterogeneous data and storing the results. The research problem we face in this paper is the proposition of an innovative distributed architecture to tackle the above-mentioned problems. The architecture uses state-of-the-art technologies with a focus on efficiency, scalability and also openness, so that community-created components and digital content analyzers could be added. Moreover, we prove the usability of the prototype on Kaggle Fake News dataset. In particular, we consider different configurations of the proposed deep neural network and present the results reflecting the effectiveness, scalability and transferability of the proposed solution.