The exponential growth of social media has brought with it an increasing propagation of hate speech and hate based propaganda. Hate speech is commonly defined as any communication that disparages a person or a group on the basis of some characteristics such as race, colour, ethnicity, gender, sexual orientation, nationality, religion. Online hate diffusion has now developed into a serious problem and this has led to a number of international initiatives being proposed, aimed at qualifying the problem and developing effective counter-measures. The aim of this paper is to analyse the knowledge structure of hate speech literature and the evolution of related topics. We apply co-word analysis methods to identify different topics treated in the field. The analysed database was downloaded from Scopus, focusing on a number of publications during the last thirty years. Topic and network analyses of literature showed that the main research topics can be divided into three areas: “general debate hate speech versus freedom of expression”,“hate-speech automatic detection and classification by machine-learning strategies”, and “gendered hate speech and cyberbullying”. The understanding of how research fronts interact led to stress the relevance of machine learning approaches to correctly assess hatred forms of online speech.

中文翻译：

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仇恨言论三十年研究：感兴趣的话题及其演变

社交媒体的指数级增长带来了仇恨言论和基于仇恨的宣传越来越多的传播。仇恨言论通常被定义为任何基于种族、肤色、民族、性别、性取向、国籍、宗教等特征贬低个人或群体的交流。在线仇恨传播现在已经发展成为一个严重的问题，这导致提出了一些国际倡议，旨在确定问题并制定有效的对策。本文的目的是分析仇恨言论文献的知识结构和相关话题的演变。我们应用共词分析方法来识别该领域处理的不同主题。分析的数据库是从 Scopus 下载的，在过去的三十年中专注于一些出版物。对文献的话题和网络分析表明，主要研究课题可以分为三个领域：“一般性辩论仇恨言论与言论自由”、“机器学习策略对仇恨言论的自动检测和分类”和“性别仇恨言论”和网络欺凌”。对研究前沿如何相互作用的理解导致强调机器学习方法在正确评估在线言论的仇恨形式方面的相关性。