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.- Anthology ID:
- 2023.unlp-1.5
- Volume:
- Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Editor:
- Mariana Romanyshyn
- Venue:
- UNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 40–48
- Language:
- URL:
- https://aclanthology.org/2023.unlp-1.5
- DOI:
- 10.18653/v1/2023.unlp-1.5
- Cite (ACL):
- Veronika Solopova, Christoph Benzmüller, and Tim Landgraf. 2023. The Evolution of Pro-Kremlin Propaganda From a Machine Learning and Linguistics Perspective. In Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP), pages 40–48, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- The Evolution of Pro-Kremlin Propaganda From a Machine Learning and Linguistics Perspective (Solopova et al., UNLP 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.unlp-1.5.pdf