@InProceedings{saleh-EtAl:2019:S19-2,
  author    = {Saleh, Abdelrhman  and  Baly, Ramy  and  Barrón-Cedeño, Alberto  and  Da San Martino, Giovanni  and  Mohtarami, Mitra  and  Nakov, Preslav  and  Glass, James},
  title     = {Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets 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     = {1041--1046},
  abstract  = {We describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection. We rely on a variety of engineered features originally used to detect propaganda. This is based on the assumption that biased messages are propagandistic and promote a particular political cause or viewpoint. In particular, we trained a logistic regression model with features ranging from simple bag of words to vocabulary richness and text readability. Our system achieved 72.9\% accuracy on the manually annotated testset, and 60.8\% on the test data that was obtained with distant supervision. Additional experiments showed that significant performance gains can be achieved with better feature pre-processing.},
  url       = {http://www.aclweb.org/anthology/S19-2182}
}