@inproceedings{solopova-etal-2023-evolution,
    title = "The Evolution of Pro-Kremlin Propaganda From a Machine Learning and Linguistics Perspective",
    author = {Solopova, Veronika  and
      Benzm{\"u}ller, Christoph  and
      Landgraf, Tim},
    editor = "Romanyshyn, Mariana",
    booktitle = "Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.unlp-1.5/",
    doi = "10.18653/v1/2023.unlp-1.5",
    pages = "40--48",
    abstract = "In the Russo-Ukrainian war, propaganda is produced by Russian state-run news outlets for both international and domestic audiences. Its content and form evolve and change with time as the war continues. This constitutes a challenge to content moderation tools based on machine learning when the data used for training and the current news start to differ significantly. In this follow-up study, we evaluate our previous BERT and SVM models that classify Pro-Kremlin propaganda from a Pro-Western stance, trained on the data from news articles and telegram posts at the start of 2022, on the new 2023 subset. We examine both classifiers' errors and perform a comparative analysis of these subsets to investigate which changes in narratives provoke drops in performance."
}