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        <title>Detecting Online Hate Speech Using Context Aware Models</title>
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        <namePart type="given">Lei</namePart>
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    <name type="personal">
        <namePart type="given">Ruihong</namePart>
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            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
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    <abstract>In the wake of a polarizing election, the cyber world is laden with hate speech. Context accompanying a hate speech text is useful for identifying hate speech, which however has been largely overlooked in existing datasets and hate speech detection models. In this paper, we provide an annotated corpus of hate speech with context information well kept. Then we propose two types of hate speech detection models that incorporate context information, a logistic regression model with context features and a neural network model with learning components for context. Our evaluation shows that both models outperform a strong baseline by around 3% to 4% in F1 score and combining these two models further improve the performance by another 7% in F1 score.</abstract>
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%0 Conference Proceedings
%T Detecting Online Hate Speech Using Context Aware Models
%A Gao, Lei
%A Huang, Ruihong
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F gao-huang-2017-detecting
%X In the wake of a polarizing election, the cyber world is laden with hate speech. Context accompanying a hate speech text is useful for identifying hate speech, which however has been largely overlooked in existing datasets and hate speech detection models. In this paper, we provide an annotated corpus of hate speech with context information well kept. Then we propose two types of hate speech detection models that incorporate context information, a logistic regression model with context features and a neural network model with learning components for context. Our evaluation shows that both models outperform a strong baseline by around 3% to 4% in F1 score and combining these two models further improve the performance by another 7% in F1 score.
%R 10.26615/978-954-452-049-6_036
%U https://doi.org/10.26615/978-954-452-049-6_036
%P 260-266

Markdown (Informal)

[Detecting Online Hate Speech Using Context Aware Models](https://doi.org/10.26615/978-954-452-049-6_036) (Gao & Huang, RANLP 2017)

• Detecting Online Hate Speech Using Context Aware Models (Gao & Huang, RANLP 2017)

ACL

• Lei Gao and Ruihong Huang. 2017. Detecting Online Hate Speech Using Context Aware Models. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 260–266, Varna, Bulgaria. INCOMA Ltd..