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The article describes a fast solution to propaganda detection at SemEval-2020Task 11, based onfeature adjustment. We use per-token vectorization of featuresand a simple Logistic Regressionclassifier to quickly test different hypothesesabout our data. We come up with what seems to usthe best solution, however, weare unable to align it with the result of the metric suggested by theorganizersof the task. We test how our system handles class and feature imbalance byvarying thenumber of samples of two classes (Propaganda and None) in thetraining set, the size of a contextwindow in which a token is vectorized andcombination of vectorization means. The result of oursystem at SemEval2020 Task11 is F-score=0.37.

UTMN at SemEval-2020 Task 11: A Kitchen Solution to Automatic Propaganda Detection