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        <title>A Platform Agnostic Dual-Strand Hate Speech Detector</title>
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        <namePart type="given">Johannes</namePart>
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            <title>Proceedings of the Third Workshop on Abusive Language Online</title>
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            <namePart type="given">Sarah</namePart>
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            <namePart type="family">Roberts</namePart>
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            <namePart type="given">Vinodkumar</namePart>
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    <abstract>Hate speech detectors must be applicable across a multitude of services and platforms, and there is hence a need for detection approaches that do not depend on any information specific to a given platform. For instance, the information stored about the text’s author may differ between services, and so using such data would reduce a system’s general applicability. The paper thus focuses on using exclusively text-based input in the detection, in an optimised architecture combining Convolutional Neural Networks and Long Short-Term Memory-networks. The hate speech detector merges two strands with character n-grams and word embeddings to produce the final classification, and is shown to outperform comparable previous approaches.</abstract>
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%0 Conference Proceedings
%T A Platform Agnostic Dual-Strand Hate Speech Detector
%A Meyer, Johannes Skjeggestad
%A Gambäck, Björn
%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 meyer-gamback-2019-platform
%X Hate speech detectors must be applicable across a multitude of services and platforms, and there is hence a need for detection approaches that do not depend on any information specific to a given platform. For instance, the information stored about the text’s author may differ between services, and so using such data would reduce a system’s general applicability. The paper thus focuses on using exclusively text-based input in the detection, in an optimised architecture combining Convolutional Neural Networks and Long Short-Term Memory-networks. The hate speech detector merges two strands with character n-grams and word embeddings to produce the final classification, and is shown to outperform comparable previous approaches.
%R 10.18653/v1/W19-3516
%U https://aclanthology.org/W19-3516/
%U https://doi.org/10.18653/v1/W19-3516
%P 146-156

Markdown (Informal)

[A Platform Agnostic Dual-Strand Hate Speech Detector](https://aclanthology.org/W19-3516/) (Meyer & Gambäck, ALW 2019)

• A Platform Agnostic Dual-Strand Hate Speech Detector (Meyer & Gambäck, ALW 2019)

ACL

• Johannes Skjeggestad Meyer and Björn Gambäck. 2019. A Platform Agnostic Dual-Strand Hate Speech Detector. In Proceedings of the Third Workshop on Abusive Language Online, pages 146–156, Florence, Italy. Association for Computational Linguistics.