@InProceedings{gao-kuppersmith-huang:2017:I17-1,
  author    = {Gao, Lei  and  Kuppersmith, Alexis  and  Huang, Ruihong},
  title     = {Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {774--782},
  abstract  = {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.},
  url       = {http://www.aclweb.org/anthology/I17-1078}
}