  The paper is devoted to the participation of the TUDublin team in
Constraint@AAAI2021 - COVID19 Fake News Detection Challenge. Today, the problem
of fake news detection is more acute than ever in connection with the pandemic.
The number of fake news is increasing rapidly and it is necessary to create AI
tools that allow us to identify and prevent the spread of false information
about COVID-19 urgently. The main goal of the work was to create a model that
would carry out a binary classification of messages from social media as real
or fake news in the context of COVID-19. Our team constructed the ensemble
consisting of Bidirectional Long Short Term Memory, Support Vector Machine,
Logistic Regression, Naive Bayes and a combination of Logistic Regression and
Naive Bayes. The model allowed us to achieve 0.94 F1-score, which is within 5\%
of the best result.
