This dataset includes multilingual content from forums, Telegram, social media, and other sources in English, French, and Arabic. It covers various radical ideologies and is pseudonymized to protect privacy while maintaining data utility. It includes annotations for call to action, radicalization level, and named entity recognition.
If you use this work, please cite the following:
@inproceedings{riabi-etal-2024-cloaked,
abstract = {Protecting privacy is essential when sharing data, particularly in the case of an online radicalization dataset that may contain personal information. In this paper, we explore the balance between preserving data usefulness and ensuring robust privacy safeguards, since regulations like the European GDPR shape how personal information must be handled. We share our method for manually pseudonymizing a multilingual radicalization dataset, ensuring performance comparable to the original data. Furthermore, we highlight the importance of establishing comprehensive guidelines for processing sensitive NLP data by sharing our complete pseudonymization process, our guidelines, the challenges we encountered as well as the resulting dataset.},
address = {Bangkok, Thailand},
title = {Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks},
author = {Riabi, Arij and Mahamdi, Menel and Mouilleron, Virginie and Seddah, Djam{\'e}},
year = {2024},
booktitle = {Proceedings of the Fifth Workshop on Privacy in Natural Language Processing},
publisher = {Association for Computational Linguistics},
editor = {Habernal, Ivan and Ghanavati, Sepideh and Ravichander, Abhilasha and Jain, Vijayanta and Thaine, Patricia and Igamberdiev, Timour and Mireshghallah, Niloofar and Feyisetan, Oluwaseyi},
pages = {123--136},
url = {https://aclanthology.org/2024.privatenlp-1.13},
hal_url = {https://inria.hal.science/hal-04624789v2/file/Private_NLP-7},
hal_pdf = {https://inria.hal.science/hal-04624789v2/file/Private_NLP-7.pdf},
}@inproceedings{riabi-etal-2025-beyond,
address = {Abu Dhabi, UAE},
author = {Riabi, Arij and Mouilleron, Virginie and Mahamdi, Menel and Antoun, Wissam and Seddah, Djam{\'e}},
title = {Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection},
year = {2025},
booktitle = {Proceedings of the 31st International Conference on Computational Linguistics},
publisher = {Association for Computational Linguistics},
editor = {Rambow, Owen and Wanner, Leo and Apidianaki, Marianna and Al-Khalifa, Hend and Eugenio, Barbara Di and Schockaert, Steven},
pages = {8640--8663},
url = {https://aclanthology.org/2025.coling-main.578/},
hal_url = {https://hal.science/hal-04867863},
hal_pdf = {https://hal.science/hal-04867863v1/file/hal.pdf},
}