Hyperpartisan News Detection with GloVe vectors and SVM

Chia-Lun Yeh and Babak Loni and Anne Schuth. In Proceedings of SEMEVAL'19, 2019.

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 SemEval2019 task. The code is released for reproducibility.

Links

Hyperpartisan News Detection with GloVe vectors and SVM
https://github.com/chialun-yeh/SemEval2019

Bib

@inproceedings{yeh2019,
  title = {Hyperpartisan News Detection with GloVe vectors and SVM},
  author = {Chia-Lun Yeh and Babak Loni and Anne Schuth},
  year = {2019},
  booktitle = {Proceedings of SEMEVAL'19}
}