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Deep learning for hate speech detection: a comparative study
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AI
DOI：10.1007/s41060-024-00650-6.png)
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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 effectiveness, 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.
Keyword:
Hate speech detection
Natural language processing
Deep learning
Machine learning
Transformers
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I
IF:
2.8
论文数:
1.1K
被引数:
1.3K
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