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    <titleInfo>
        <title>Using Convolutional Neural Networks to Classify Hate-Speech</title>
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    <name type="personal">
        <namePart type="given">Björn</namePart>
        <namePart type="family">Gambäck</namePart>
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        <namePart type="given">Utpal</namePart>
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            <title>Proceedings of the First Workshop on Abusive Language Online</title>
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        <name type="personal">
            <namePart type="given">Zeerak</namePart>
            <namePart type="family">Waseem</namePart>
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        <name type="personal">
            <namePart type="given">Wendy</namePart>
            <namePart type="given">Hui</namePart>
            <namePart type="given">Kyong</namePart>
            <namePart type="family">Chung</namePart>
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            <namePart type="given">Dirk</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/W17-3013</identifier>
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        <date>2017-08</date>
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%0 Conference Proceedings
%T Using Convolutional Neural Networks to Classify Hate-Speech
%A Gambäck, Björn
%A Sikdar, Utpal Kumar
%Y Waseem, Zeerak
%Y Chung, Wendy Hui Kyong
%Y Hovy, Dirk
%Y Tetreault, Joel
%S Proceedings of the First Workshop on Abusive Language Online
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, BC, Canada
%F gamback-sikdar-2017-using
%X 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.
%R 10.18653/v1/W17-3013
%U https://aclanthology.org/W17-3013
%U https://doi.org/10.18653/v1/W17-3013
%P 85-90

Markdown (Informal)

[Using Convolutional Neural Networks to Classify Hate-Speech](https://aclanthology.org/W17-3013) (Gambäck & Sikdar, ALW 2017)

• Using Convolutional Neural Networks to Classify Hate-Speech (Gambäck & Sikdar, ALW 2017)

ACL

• Björn Gambäck and Utpal Kumar Sikdar. 2017. Using Convolutional Neural Networks to Classify Hate-Speech. In Proceedings of the First Workshop on Abusive Language Online, pages 85–90, Vancouver, BC, Canada. Association for Computational Linguistics.