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.

翻译：自动化仇恨言论检测是遏制仇恨言论传播的重要工具，尤其在社交媒体领域。已有多种方法被开发用于该任务，其中近期涌现了大量基于深度学习的方法。研究团队还构建了多样化的数据集，体现了仇恨言论检测问题的不同表现形式。本文通过三个最常用数据集，对深度与浅层仇恨言论检测方法进行了大规模实证比较。我们的目标是阐明该领域的研究进展，并识别当前最优方法的优势与不足。分析重点聚焦实际性能指标，包括检测准确率、计算效率、预训练模型应用能力以及领域泛化性。通过此项研究，我们旨在为仇恨言论检测的实际应用提供指导，量化当前最优水平，并确定未来研究方向。代码与数据集可通过 https://github.com/jmjmalik22/Hate-Speech-Detection 获取。