@inproceedings{devatine-etal-2022-predicting,
    title = "Predicting Political Orientation in News with Latent Discourse Structure to Improve Bias Understanding",
    author = "Devatine, Nicolas  and
      Muller, Philippe  and
      Braud, Chlo{\'e}",
    editor = "Braud, Chloe  and
      Hardmeier, Christian  and
      Li, Junyi Jessy  and
      Loaiciga, Sharid  and
      Strube, Michael  and
      Zeldes, Amir",
    booktitle = "Proceedings of the 3rd Workshop on Computational Approaches to Discourse",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea and Online",
    publisher = "International Conference on Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.codi-1.10/",
    pages = "77--85",
    abstract = "With the growing number of information sources, the problem of media bias becomes worrying for a democratic society. This paper explores the task of predicting the political orientation of news articles, with a goal of analyzing how bias is expressed. We demonstrate that integrating rhetorical dimensions via latent structures over sub-sentential discourse units allows for large improvements, with a +7.4 points difference between the base LSTM model and its discourse-based version, and +3 points improvement over the previous BERT-based state-of-the-art model. We also argue that this gives a new relevant handle for analyzing political bias in news articles."
}