@InProceedings{gao-huang:2017:RANLP,
  author    = {Gao, Lei  and  Huang, Ruihong},
  title     = {Detecting Online Hate Speech Using Context Aware Models},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {260--266},
  abstract  = {In the wake of a polarizing election, the cyber world is laden with hate
	speech. Context  
	  accompanying a hate speech text is useful for identifying hate speech, which
	however 
	  has been largely overlooked in existing datasets and hate speech detection
	models. In this paper, we provide an annotated corpus of hate speech  
	  with context information well kept. Then we propose two types of hate speech
	detection models that incorporate context information, a logistic regression
	model with context features and a neural network model with learning components
	for context. Our evaluation shows that both models outperform a strong baseline
	by around 3% to 4% in F1 score and combining these two models further improve
	the performance by another 7% in F1 score.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_036}
}