  The article describes a fast solution to propaganda detection at SemEval-2020
Task 11, based onfeature adjustment. We use per-token vectorization of features
and a simple Logistic Regressionclassifier to quickly test different hypotheses
about our data. We come up with what seems to usthe best solution, however, we
are unable to align it with the result of the metric suggested by theorganizers
of the task. We test how our system handles class and feature imbalance by
varying thenumber of samples of two classes (Propaganda and None) in the
training set, the size of a contextwindow in which a token is vectorized and
combination of vectorization means. The result of oursystem at SemEval2020 Task
11 is F-score=0.37.
