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Counter DataSet Public | Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks (

<h1>Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks (Counter DataSet)</h1> <p>Official repository of Counter DataSet, the pseudoanonymized dataset for Radicalization Detection with Named Entity Recognition annotations. You can read the paper <a>here</a></p> <p>Annotated examples for every language are avilable in the folder 'Examples'.</p> <p><em><strong>WARNING: The datasets contain content that is racist, sexist, homophobic, and offensive in many other ways.</strong></em></p> <p>Training and test sets available filling in this <strong><a href="https://forms.gle/7bprpid1fFqKakrz6" target="_blank" rel="nofollow noreferrer noopener">form</a>; </strong>an email notification will be sent with instructions and details about how to download the data.</p> <p><em><strong>Please cite our paper in any published work that uses any of these resources.</strong></em></p> <div> <pre><code>@inproceedings{, title = {Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks}, author = {Arij Riabi, Menel Mahamdi, Virginie Mouilleron, Djam&eacute; Seddah}, booktitle = {Proceedings of the fifth Workshop on Privacy in Natural Language Processing}, year = {2024}, location = {Bangkok, Thailand}, }</code></pre> </div> <p><em><strong>Contact</strong></em></p> <p>If you have any questions please contact <code>djame dot seddah at inria dot fr</code> or <code>arij dot riabi at inria dot fr</code>.</p> <p>Maintainers: <code>djame dot seddah at inria dot fr</code> <code>arijriabi96 at gmail dot com</code></p> <p>https://counter-project.eu/</p> <p><code>This project has received funding from the European Union&rsquo;s Horizon 2020 research and innovation programme under grant agreement No. 101021607. The contents of this website are the sole responsibility of the CounteR consortium and can in no way be taken to reflect the views of the European Union.</code></p>

ShareScore

32/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
16
Reuse readiness
8
Engagement
0