%0 Conference Proceedings %T Vernon-fenwick at SemEval-2019 Task 4: Hyperpartisan News Detection using Lexical and Semantic Features %A Srivastava, Vertika %A Gupta, Ankita %A Prakash, Divya %A Sahoo, Sudeep Kumar %A R.R, Rohit %A Kim, Yeon Hyang %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 srivastava-etal-2019-vernon %X 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. %R 10.18653/v1/S19-2189 %U https://aclanthology.org/S19-2189 %U https://doi.org/10.18653/v1/S19-2189 %P 1078-1082