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    <titleInfo>
        <title>Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter</title>
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    <name type="personal">
        <namePart type="given">Muhammad</namePart>
        <namePart type="given">Okky</namePart>
        <namePart type="family">Ibrohim</namePart>
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    <name type="personal">
        <namePart type="given">Indra</namePart>
        <namePart type="family">Budi</namePart>
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            <title>Proceedings of the Third Workshop on Abusive Language Online</title>
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        <name type="personal">
            <namePart type="given">Sarah</namePart>
            <namePart type="given">T</namePart>
            <namePart type="family">Roberts</namePart>
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        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
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        <name type="personal">
            <namePart type="given">Vinodkumar</namePart>
            <namePart type="family">Prabhakaran</namePart>
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    <abstract>Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflict between citizen. Moreover, hate speech has a target, category, and level that also needs to be detected to help the authority in prioritizing which hate speech must be addressed immediately. This research discusses multi-label text classification for abusive language and hate speech detection including detecting the target, category, and level of hate speech in Indonesian Twitter using machine learning approach with Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) classifier and Binary Relevance (BR), Label Power-set (LP), and Classifier Chains (CC) as the data transformation method. We used several kinds of feature extractions which are term frequency, orthography, and lexicon features. Our experiment results show that in general RFDT classifier using LP as the transformation method gives the best accuracy with fast computational time.</abstract>
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    <identifier type="doi">10.18653/v1/W19-3506</identifier>
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        <date>2019-08</date>
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%0 Conference Proceedings
%T Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter
%A Ibrohim, Muhammad Okky
%A Budi, Indra
%Y Roberts, Sarah T.
%Y Tetreault, Joel
%Y Prabhakaran, Vinodkumar
%Y Waseem, Zeerak
%S Proceedings of the Third Workshop on Abusive Language Online
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F ibrohim-budi-2019-multi
%X Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflict between citizen. Moreover, hate speech has a target, category, and level that also needs to be detected to help the authority in prioritizing which hate speech must be addressed immediately. This research discusses multi-label text classification for abusive language and hate speech detection including detecting the target, category, and level of hate speech in Indonesian Twitter using machine learning approach with Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) classifier and Binary Relevance (BR), Label Power-set (LP), and Classifier Chains (CC) as the data transformation method. We used several kinds of feature extractions which are term frequency, orthography, and lexicon features. Our experiment results show that in general RFDT classifier using LP as the transformation method gives the best accuracy with fast computational time.
%R 10.18653/v1/W19-3506
%U https://aclanthology.org/W19-3506/
%U https://doi.org/10.18653/v1/W19-3506
%P 46-57

Markdown (Informal)

[Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter](https://aclanthology.org/W19-3506/) (Ibrohim & Budi, ALW 2019)

• Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter (Ibrohim & Budi, ALW 2019)

ACL

• Muhammad Okky Ibrohim and Indra Budi. 2019. Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter. In Proceedings of the Third Workshop on Abusive Language Online, pages 46–57, Florence, Italy. Association for Computational Linguistics.