Tom Jumbo-Grumbo at SemEval-2019 Task 4: Hyperpartisan News Detection with GloVe vectors and SVM
In this paper, we describe our attempt to learn bias from news articles. From our experiments, it seems that although there is a correlation between publisher bias and article bias, it is challenging to learn bias directly from the publisher labels. On the other hand, using few manually-labeled samples can increase the accuracy metric from around 60{\%} to near 80{\%}. Our system is computationally inexpensive and uses several standard document representations in NLP to train an SVM or LR classifier. The system ranked 4th in the SemEval-2019 task. The code is released for reproducibility.
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