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arXiv · 2202.09517

Deep Learning for Hate Speech Detection: A Comparative Study

Jitendra Singh Malik·Hezhe Qiao·Guansong Pang·Anton van den Hengel
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Abstract

Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media. Numerous methods have been developed for the task, including a recent proliferation of deep-learning based approaches. A variety of datasets have also been developed, exemplifying various manifestations of the hate-speech detection problem. We present here a large-scale empirical comparison of deep and shallow hate-speech detection methods, mediated through the three most commonly used datasets. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. We particularly focus our analysis on measures of practical performance, including detection accuracy, computational efficiency, capability in using pre-trained models, and domain generalization. In doing so we aim to provide guidance as to the use of hate-speech detection in practice, quantify the state-of-the-art, and identify future research directions. Code and dataset are available at https://github.com/jmjmalik22/Hate-Speech-Detection.

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Jitendra Singh Malik, Hezhe Qiao, Guansong Pang, Anton van den Hengel. 2022-02-19. Deep Learning for Hate Speech Detection: A Comparative Study. https://arxiv.org/abs/2202.09517

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