@inproceedings{srivastava-etal-2019-vernon,
    title = "Vernon-fenwick at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection using Lexical and Semantic Features",
    author = "Srivastava, Vertika  and
      Gupta, Ankita  and
      Prakash, Divya  and
      Sahoo, Sudeep Kumar  and
      R.R, Rohit  and
      Kim, Yeon Hyang",
    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://preview.aclanthology.org/jlcl-multiple-ingestion/S19-2189/",
    doi = "10.18653/v1/S19-2189",
    pages = "1078--1082",
    abstract = "In this paper, we present our submission for SemEval-2019 Task 4: Hyperpartisan News Detection. Hyperpartisan news articles are sharply polarized and extremely biased (onesided). It shows blind beliefs, opinions and unreasonable adherence to a party, idea, faction or a person. Through this task, we aim to develop an automated system that can be used to detect hyperpartisan news and serve as a prescreening technique for fake news detection. The proposed system jointly uses a rich set of handcrafted textual and semantic features. Our system achieved 2nd rank on the primary metric (82.0{\%} accuracy) and 1st rank on the secondary metric (82.1{\%} F1-score), among all participating teams. Comparison with the best performing system on the leaderboard shows that our system is behind by only 0.2{\%} absolute difference in accuracy."
}