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.
@inproceedings{yeh-etal-2019-tom, title = "Tom Jumbo-Grumbo at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection with {G}lo{V}e vectors and {SVM}", author = "Yeh, Chia-Lun and Loni, Babak and Schuth, Anne", editor = "May, Jonathan and Shutova, Ekaterina and Herbelot, Aurelie and Zhu, Xiaodan and Apidianaki, Marianna and Mohammad, Saif M.", booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation", month = jun, year = "2019", address = "Minneapolis, Minnesota, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/S19-2187/", doi = "10.18653/v1/S19-2187", pages = "1067--1071", abstract = "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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%0 Conference Proceedings%T Tom Jumbo-Grumbo at SemEval-2019 Task 4: Hyperpartisan News Detection with GloVe vectors and SVM%A Yeh, Chia-Lun%A Loni, Babak%A Schuth, Anne%Y May, Jonathan%Y Shutova, Ekaterina%Y Herbelot, Aurelie%Y Zhu, Xiaodan%Y Apidianaki, Marianna%Y Mohammad, Saif M.%S Proceedings of the 13th International Workshop on Semantic Evaluation%D 2019%8 June%I Association for Computational Linguistics%C Minneapolis, Minnesota, USA%F yeh-etal-2019-tom%X 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.%R 10.18653/v1/S19-2187%U https://aclanthology.org/S19-2187/%U https://doi.org/10.18653/v1/S19-2187%P 1067-1071
[Tom Jumbo-Grumbo at SemEval-2019 Task 4: Hyperpartisan News Detection with GloVe vectors and SVM](https://aclanthology.org/S19-2187/) (Yeh et al., SemEval 2019)