@inproceedings{gamback-sikdar-2017-using,
    title = "Using Convolutional Neural Networks to Classify Hate-Speech",
    author = {Gamb{\"a}ck, Bj{\"o}rn  and
      Sikdar, Utpal Kumar},
    editor = "Waseem, Zeerak  and
      Chung, Wendy Hui Kyong  and
      Hovy, Dirk  and
      Tetreault, Joel",
    booktitle = "Proceedings of the First Workshop on Abusive Language Online",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W17-3013/",
    doi = "10.18653/v1/W17-3013",
    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."
}