A Weakly Supervised Classifier and Dataset of White Supremacist Language
Michael Miller Yoder Ahmad Diab David West Brown Kathleen M. Carley
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.