@inproceedings{farber-etal-2019-team,
    title = "Team Peter Brinkmann at {S}em{E}val-2019 Task 4: Detecting Biased News Articles Using Convolutional Neural Networks",
    author = {F{\"a}rber, Michael  and
      Qurdina, Agon  and
      Ahmedi, Lule},
    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/add-emnlp-2024-awards/S19-2180/",
    doi = "10.18653/v1/S19-2180",
    pages = "1032--1036",
    abstract = "In this paper, we present an approach for classifying news articles as biased (i.e., hyperpartisan) or unbiased, based on a convolutional neural network. We experiment with various embedding methods (pretrained and trained on the training dataset) and variations of the convolutional neural network architecture and compare the results. When evaluating our best performing approach on the actual test data set of the SemEval 2019 Task 4, we obtained relatively low precision and accuracy values, while gaining the highest recall rate among all 42 participating teams."
}