@inproceedings{isbister-johansson-2019-dick,
    title = "Dick-Preston and Morbo at {S}em{E}val-2019 Task 4: Transfer Learning for Hyperpartisan News Detection",
    author = "Isbister, Tim  and
      Johansson, Fredrik",
    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/fix-sig-urls/S19-2160/",
    doi = "10.18653/v1/S19-2160",
    pages = "939--943",
    abstract = "In a world of information operations, influence campaigns, and fake news, classification of news articles as following hyperpartisan argumentation or not is becoming increasingly important. We present a deep learning-based approach in which a pre-trained language model has been fine-tuned on domain-specific data and used for classification of news articles, as part of the SemEval-2019 task on hyperpartisan news detection. The suggested approach yields accuracy and F1-scores around 0.8 which places the best performing classifier among the top-5 systems in the competition."
}