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In this paper, we describe our submission to SemEval-2019 Task 4 onHyperpartisan News Detection. Our system relies on a variety of engineeredfeatures originally used to detect propaganda. This is based on the assumptionthat biased messages are propagandistic in the sense that they promote aparticular political cause or viewpoint. We trained a logistic regression modelwith features ranging from simple bag-of-words to vocabulary richness and textreadability features. Our system achieved 72.9% accuracy on the test data thatis annotated manually and 60.8% on the test data that is annotated with distantsupervision. Additional experiments showed that significant performanceimprovements can be achieved with better feature pre-processing.

Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection