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        <title>The UNLP 2025 Shared Task on Detecting Social Media Manipulation</title>
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    <name type="personal">
        <namePart type="given">Roman</namePart>
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    <name type="personal">
        <namePart type="given">Nataliia</namePart>
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    <name type="personal">
        <namePart type="given">Volodymyr</namePart>
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            <title>Proceedings of the Fourth Ukrainian Natural Language Processing Workshop (UNLP 2025)</title>
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            <namePart type="given">Mariana</namePart>
            <namePart type="family">Romanyshyn</namePart>
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    <abstract>This paper presents the results of the UNLP 2025 Shared Task on Detecting Social Media Manipulation. The task included two tracks: Technique Classification and Span Identification. The benchmark dataset contains 9,557 posts from Ukrainian Telegram channels manually annotated by media experts. A total of 51 teams registered, 22 teams submitted systems, and 595 runs were evaluated on a hidden test set via Kaggle. Performance was measured with macro F1 for classification and token-level F1 for identification. The shared task provides the first publicly available benchmark for manipulation detection in Ukrainian social media and highlights promising directions for low-resource propaganda research. The Kaggle leaderboard is left open for further submissions.</abstract>
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    <identifier type="doi">10.18653/v1/2025.unlp-1.12</identifier>
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%0 Conference Proceedings
%T The UNLP 2025 Shared Task on Detecting Social Media Manipulation
%A Kyslyi, Roman
%A Romanyshyn, Nataliia
%A Sydorskyi, Volodymyr
%Y Romanyshyn, Mariana
%S Proceedings of the Fourth Ukrainian Natural Language Processing Workshop (UNLP 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria (online)
%@ 979-8-89176-269-5
%F kyslyi-etal-2025-unlp
%X This paper presents the results of the UNLP 2025 Shared Task on Detecting Social Media Manipulation. The task included two tracks: Technique Classification and Span Identification. The benchmark dataset contains 9,557 posts from Ukrainian Telegram channels manually annotated by media experts. A total of 51 teams registered, 22 teams submitted systems, and 595 runs were evaluated on a hidden test set via Kaggle. Performance was measured with macro F1 for classification and token-level F1 for identification. The shared task provides the first publicly available benchmark for manipulation detection in Ukrainian social media and highlights promising directions for low-resource propaganda research. The Kaggle leaderboard is left open for further submissions.
%R 10.18653/v1/2025.unlp-1.12
%U https://aclanthology.org/2025.unlp-1.12/
%U https://doi.org/10.18653/v1/2025.unlp-1.12
%P 105-111

Markdown (Informal)

[The UNLP 2025 Shared Task on Detecting Social Media Manipulation](https://aclanthology.org/2025.unlp-1.12/) (Kyslyi et al., UNLP 2025)

• The UNLP 2025 Shared Task on Detecting Social Media Manipulation (Kyslyi et al., UNLP 2025)

ACL

• Roman Kyslyi, Nataliia Romanyshyn, and Volodymyr Sydorskyi. 2025. The UNLP 2025 Shared Task on Detecting Social Media Manipulation. In Proceedings of the Fourth Ukrainian Natural Language Processing Workshop (UNLP 2025), pages 105–111, Vienna, Austria (online). Association for Computational Linguistics.