Solopova, VeronikaVeronikaSolopovaBenzmüller, ChristophChristophBenzmüller0000-0002-3392-3093Landgraf, TimTimLandgraf2023-07-312023-07-312023978-1-959429-52-4https://fis.uni-bamberg.de/handle/uniba/59601In 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.engPropaganda004conferenceobject10.18653/v1/2023.unlp-1.5