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• W2953647525 abstract "We describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection. We rely on a variety of engineered features originally used to detect propaganda. This is based on the assumption that biased messages are propagandistic and promote a particular political cause or viewpoint. In particular, we trained a logistic regression model with features ranging from simple bag of words to vocabulary richness and text readability. Our system achieved 72.9% accuracy on the manually annotated testset, and 60.8% on the test data that was obtained with distant supervision. Additional experiments showed that significant performance gains can be achieved with better feature pre-processing." @default.
• W2953647525 created "2019-07-12" @default.
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• W2953647525 date "2019-01-01" @default.
• W2953647525 modified "2023-10-12" @default.
• W2953647525 title "Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection" @default.
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• W2953647525 doi "https://doi.org/10.18653/v1/s19-2182" @default.
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