Fake News Detection from Data Streams

Using fake news as a political or economic tool is not new, but the scale of their use is currently alarming, especially on social media. The authors of misinformation try to influence the users' decisions, both in the economic and political sphere. The facts of using disinformation during elections are well known. Currently, two fake news detection approaches dominate. The first approach, so-called fact or news checker, is based on the knowledge and work of volunteers, the second approach employs artificial intelligence algorithms for news analysis and manipulation detection. In this work, we will focus on using machine learning methods to detect fake news. However, unlike most approaches, we will treat incoming messages as stream data, taking into account the possibility of concept drift occurring, i.e., appearing changes in the probabilistic characteristics of the classification model during the exploitation of the classifier. The developed methods have been evaluated based on computer experiments on benchmark data, and the obtained results prove their usefulness for the problem under consideration. The proposed solutions are part of the distributed platform developed by the H2020 SocialTruth project consortium.