  In the wake of a polarizing election, social media is laden with hateful
content. To address various limitations of supervised hate speech
classification methods including corpus bias and huge cost of annotation, we
propose a weakly supervised two-path bootstrapping approach for an online hate
speech detection model leveraging large-scale unlabeled data. This system
significantly outperforms hate speech detection systems that are trained in a
supervised manner using manually annotated data. Applying this model on a large
quantity of tweets collected before, after, and on election day reveals
motivations and patterns of inflammatory language.
