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        <title>The Evolution of Pro-Kremlin Propaganda From a Machine Learning and Linguistics Perspective</title>
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    <name type="personal">
        <namePart type="given">Veronika</namePart>
        <namePart type="family">Solopova</namePart>
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        <namePart type="given">Tim</namePart>
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            <title>Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)</title>
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            <namePart type="given">Mariana</namePart>
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    <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.</abstract>
    <identifier type="citekey">solopova-etal-2023-evolution</identifier>
    <identifier type="doi">10.18653/v1/2023.unlp-1.5</identifier>
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        <url>https://aclanthology.org/2023.unlp-1.5/</url>
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        <date>2023-05</date>
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%0 Conference Proceedings
%T The Evolution of Pro-Kremlin Propaganda From a Machine Learning and Linguistics Perspective
%A Solopova, Veronika
%A Benzmüller, Christoph
%A Landgraf, Tim
%Y Romanyshyn, Mariana
%S Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F solopova-etal-2023-evolution
%X 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.
%R 10.18653/v1/2023.unlp-1.5
%U https://aclanthology.org/2023.unlp-1.5/
%U https://doi.org/10.18653/v1/2023.unlp-1.5
%P 40-48

Markdown (Informal)

[The Evolution of Pro-Kremlin Propaganda From a Machine Learning and Linguistics Perspective](https://aclanthology.org/2023.unlp-1.5/) (Solopova et al., UNLP 2023)

• The Evolution of Pro-Kremlin Propaganda From a Machine Learning and Linguistics Perspective (Solopova et al., UNLP 2023)

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.