A Weakly Supervised Classifier and Dataset of White Supremacist Language

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

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.