  Fake news has now grown into a big problem for societies and also a major
challenge for people fighting disinformation. This phenomenon plagues
democratic elections, reputations of individual persons or organizations, and
has negatively impacted citizens, (e.g., during the COVID-19 pandemic in the US
or Brazil). Hence, developing effective tools to fight this phenomenon by
employing advanced Machine Learning (ML) methods poses a significant challenge.
The following paper displays the present body of knowledge on the application
of such intelligent tools in the fight against disinformation. It starts by
showing the historical perspective and the current role of fake news in the
information war. Proposed solutions based solely on the work of experts are
analysed and the most important directions of the application of intelligent
systems in the detection of misinformation sources are pointed out.
Additionally, the paper presents some useful resources (mainly datasets useful
when assessing ML solutions for fake news detection) and provides a short
overview of the most important R&D projects related to this subject. The main
purpose of this work is to analyse the current state of knowledge in detecting
fake news; on the one hand to show possible solutions, and on the other hand to
identify the main challenges and methodological gaps to motivate future
research.
