@InProceedings{gamback-sikdar:2017:ALW1,
  author    = {Gamb\"{a}ck, Bj\"{o}rn  and  Sikdar, Utpal Kumar},
  title     = {Using Convolutional Neural Networks to Classify Hate-Speech},
  booktitle = {Proceedings of the First Workshop on Abusive Language Online},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, BC, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {85--90},
  abstract  = {The paper introduces a deep learning-based Twitter hate-speech text
	classification system. The classifier assigns each tweet to one of four
	predefined categories: racism, sexism, both (racism and sexism) and
	non-hate-speech. Four Convolutional Neural Network models were trained on resp.
	character 4-grams, word vectors based on semantic information built using
	word2vec, randomly generated word vectors, and word vectors combined with
	character n-grams. The feature set was down-sized in the networks by
	max-pooling, and a softmax function used to classify tweets. Tested by 10-fold
	cross-validation, the model based on word2vec embeddings performed best, with
	higher precision than recall, and a 78.3% F-score.},
  url       = {http://www.aclweb.org/anthology/W17-3013}
}