@InProceedings{gupta-EtAl:2019:S19-21,
  author    = {Gupta, Viresh  and  Kaur Jolly, Baani Leen  and  Kaur, Ramneek  and  Chakraborty, Tanmoy},
  title     = {Clark Kent at SemEval-2019 Task 4: Stylometric Insights into Hyperpartisan News Detection},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {934--938},
  abstract  = {In this paper, we present a news bias prediction system, which we developed as part of a SemEval 2019 task. We developed an XGBoost based system which uses character and word level n-gram features represented using TF-IDF, count vector based correlation matrix, and predicts if an input news article is a hyperpartisan news article. Our model was able to achieve a precision of 68.3\% on the test set provided by the contest organizers. We also run our model on the BuzzFeed corpus and find XGBoost with simple character level N-Gram embeddings to be performing well with an accuracy of around 96\%.},
  url       = {http://www.aclweb.org/anthology/S19-2159}
}