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        <title>Degree based Classification of Harmful Speech using Twitter Data</title>
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    <name type="personal">
        <namePart type="given">Sanjana</namePart>
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        <namePart type="given">Saksham</namePart>
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    <name type="personal">
        <namePart type="given">Manish</namePart>
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            <title>Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)</title>
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        <name type="personal">
            <namePart type="given">Ritesh</namePart>
            <namePart type="family">Kumar</namePart>
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        <name type="personal">
            <namePart type="given">Atul</namePart>
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            <namePart type="family">Ojha</namePart>
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    <abstract>Harmful speech has various forms and it has been plaguing the social media in different ways. If we need to crackdown different degrees of hate speech and abusive behavior amongst it, the classification needs to be based on complex ramifications which needs to be defined and hold accountable for, other than racist, sexist or against some particular group and community. This paper primarily describes how we created an ontological classification of harmful speech based on degree of hateful intent and used it to annotate twitter data accordingly. The key contribution of this paper is the new dataset of tweets we created based on ontological classes and degrees of harmful speech found in the text. We also propose supervised classification system for recognizing these respective harmful speech classes in the texts hence. This serves as a preliminary work to lay down foundation on defining different classes of harmful speech and subsequent work will be done in making it’s automatic detection more robust and efficient.</abstract>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Degree based Classification of Harmful Speech using Twitter Data
%A Sharma, Sanjana
%A Agrawal, Saksham
%A Shrivastava, Manish
%Y Kumar, Ritesh
%Y Ojha, Atul Kr.
%Y Zampieri, Marcos
%Y Malmasi, Shervin
%S Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F sharma-etal-2018-degree
%X Harmful speech has various forms and it has been plaguing the social media in different ways. If we need to crackdown different degrees of hate speech and abusive behavior amongst it, the classification needs to be based on complex ramifications which needs to be defined and hold accountable for, other than racist, sexist or against some particular group and community. This paper primarily describes how we created an ontological classification of harmful speech based on degree of hateful intent and used it to annotate twitter data accordingly. The key contribution of this paper is the new dataset of tweets we created based on ontological classes and degrees of harmful speech found in the text. We also propose supervised classification system for recognizing these respective harmful speech classes in the texts hence. This serves as a preliminary work to lay down foundation on defining different classes of harmful speech and subsequent work will be done in making it’s automatic detection more robust and efficient.
%U https://aclanthology.org/W18-4413
%P 106-112

Markdown (Informal)

[Degree based Classification of Harmful Speech using Twitter Data](https://aclanthology.org/W18-4413) (Sharma et al., TRAC 2018)

• Degree based Classification of Harmful Speech using Twitter Data (Sharma et al., TRAC 2018)

ACL

• Sanjana Sharma, Saksham Agrawal, and Manish Shrivastava. 2018. Degree based Classification of Harmful Speech using Twitter Data. In Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), pages 106–112, Santa Fe, New Mexico, USA. Association for Computational Linguistics.