It is indeed a challenge for the existing machine learning approaches to segregate the hateful content from the one that is merely offensive. One prevalent reason for low accuracy of hate detection with the current methodologies is that these techniques treat hate classification as a multiclass problem. In this article, we present the hate identification on the social media as a multilabel problem. To this end, we propose a CNN-based service framework called “HateClassify” for labeling the social media contents as the hate speech, offensive, or nonoffensive. Results demonstrate that the multiclass classification accuracy for the CNN-based approaches particularly sequential CNN (SCNN) is competitive and even higher than certain state-of-the-art classifiers. Moreover, in the multilabel classification problem, sufficiently high performance is exhibited by the SCNN among other CNN-based techniques. The results have shown that using multilabel classification instead of multiclass classification, hate speech detection is increased up to 20%.

中文翻译：

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HateClassify：社交媒体上仇恨语音识别的服务框架

对于现有的机器学习方法来说，将可憎内容与仅是令人反感的内容区分开来确实是一个挑战。当前方法中仇恨检测准确性低的一个普遍原因是，这些技术将仇恨分类视为多类问题。在本文中，我们将社交媒体上的仇恨识别作为多标签问题提出。为此，我们提出了一个基于CNN的服务框架，称为“ HateClassify”，用于将社交媒体内容标记为仇恨言论，令人反感或无礼的内容。结果表明，基于CNN的方法（尤其是顺序CNN（SCNN））的多类分类准确性具有竞争力，甚至比某些最新的分类器还高。此外，在多标签分类问题中，在其他基于CNN的技术中，SCNN表现出足够高的性能。结果表明，使用多标签分类而不是多分类分类可以将仇恨语音检测提高到20％。