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arXiv · 2306.15732

A Weakly Supervised Classifier and Dataset of White Supremacist Language

Michael Miller Yoder·Ahmad Diab·David West Brown·Kathleen M. Carley
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Abstract

We present a dataset and classifier for detecting the language of white supremacist extremism, a growing issue in online hate speech. Our weakly supervised classifier is trained on large datasets of text from explicitly white supremacist domains paired with neutral and anti-racist data from similar domains. We demonstrate that this approach improves generalization performance to new domains. Incorporating anti-racist texts as counterexamples to white supremacist language mitigates bias.

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Michael Miller Yoder, Ahmad Diab, David West Brown, Kathleen M. Carley. 2023-06-27. A Weakly Supervised Classifier and Dataset of White Supremacist Language. https://arxiv.org/abs/2306.15732

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