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        <title>Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection</title>
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            <title>Proceedings of the 31st International Conference on Computational Linguistics</title>
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    <abstract>The proliferation of radical content on online platforms poses significant risks, including inciting violence and spreading extremist ideologies. Despite ongoing research, existing datasets and models often fail to address the complexities of multilingual and diverse data. To bridge this gap, we introduce a publicly available multilingual dataset annotated with radicalization levels, calls for action, and named entities in English, French, and Arabic. This dataset is pseudonymized to protect individual privacy while preserving contextual information. Beyond presenting our freely available dataset, we analyze the annotation process, highlighting biases and disagreements among annotators and their implications for model performance. Additionally, we use synthetic data to investigate the influence of socio-demographic traits on annotation patterns and model predictions. Our work offers a comprehensive examination of the challenges and opportunities in building robust datasets for radical content detection, emphasizing the importance of fairness and transparency in model development. The Counter dataset is available at https://gitlab.inria.fr/ariabi/counter-dataset-public.</abstract>
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%0 Conference Proceedings
%T Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection
%A Riabi, Arij
%A Mouilleron, Virginie
%A Mahamdi, Menel
%A Antoun, Wissam
%A Seddah, Djamé
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F riabi-etal-2025-beyond
%X The proliferation of radical content on online platforms poses significant risks, including inciting violence and spreading extremist ideologies. Despite ongoing research, existing datasets and models often fail to address the complexities of multilingual and diverse data. To bridge this gap, we introduce a publicly available multilingual dataset annotated with radicalization levels, calls for action, and named entities in English, French, and Arabic. This dataset is pseudonymized to protect individual privacy while preserving contextual information. Beyond presenting our freely available dataset, we analyze the annotation process, highlighting biases and disagreements among annotators and their implications for model performance. Additionally, we use synthetic data to investigate the influence of socio-demographic traits on annotation patterns and model predictions. Our work offers a comprehensive examination of the challenges and opportunities in building robust datasets for radical content detection, emphasizing the importance of fairness and transparency in model development. The Counter dataset is available at https://gitlab.inria.fr/ariabi/counter-dataset-public.
%U https://aclanthology.org/2025.coling-main.578/
%P 8640-8663

Markdown (Informal)

[Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection](https://aclanthology.org/2025.coling-main.578/) (Riabi et al., COLING 2025)

• Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection (Riabi et al., COLING 2025)

ACL

• Arij Riabi, Virginie Mouilleron, Menel Mahamdi, Wissam Antoun, and Djamé Seddah. 2025. Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection. In Proceedings of the 31st International Conference on Computational Linguistics, pages 8640–8663, Abu Dhabi, UAE. Association for Computational Linguistics.