@InProceedings{hasanuzzaman-dias-way:2017:I17-1,
  author    = {Hasanuzzaman, Mohammed  and  Dias, Ga\"{e}l  and  Way, Andy},
  title     = {Demographic Word Embeddings for Racism Detection on Twitter},
  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     = {926--936},
  abstract  = {Most social media platforms grant users freedom of speech by allowing them to
	freely express their thoughts, beliefs, and opinions. Although this represents
	incredible and unique communication opportunities, it also presents important
	challenges. Online racism is such an example. In this study, we present a
	supervised learning strategy to detect racist language on Twitter based on word
	embedding that incorporate demographic (Age, Gender, and Location) information.
	Our methodology achieves reasonable classification accuracy over a gold
	standard dataset (F1=76.3%) and significantly improves over the classification
	performance of demographic-agnostic models.},
  url       = {http://www.aclweb.org/anthology/I17-1093}
}