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This paper describes our submission to task 4 in SemEval 2019, i.e., hyperpartisan news detection. Our model aims at detecting hyperpartisan news by incorporating the style-based features and the content-based features. We extract a broad number of feature sets and use as our learning algorithms the GBDT and the n-gram CNN model. Finally, we apply the weighted average for effective learning between the two models. Our model achieves an accuracy of 0.745 on the test set in subtask A.

Steve Martin at SemEval-2019 Task 4: Ensemble Learning Model for Detecting Hyperpartisan News