ABSTRACT Islamophobic hate speech on social media is a growing concern in contemporary Western politics and society. It can inflict considerable harm on any victims who are targeted, create a sense of fear and exclusion amongst their communities, toxify public discourse and motivate other forms of extremist and hateful behavior. Accordingly, there is a pressing need for automated tools to detect and classify Islamophobic hate speech robustly and at scale, thereby enabling quantitative analyses of large textual datasets, such as those collected from social media. Previous research has mostly approached the automated detection of hate speech as a binary task. However, the varied nature of Islamophobia means that this is often inappropriate for both theoretically informed social science and effective monitoring of social media platforms. Drawing on in-depth conceptual work we build an automated software tool which distinguishes between non-Islamophobic, weak Islamophobic and strong Islamophobic content. Accuracy is 77.6% and balanced accuracy is 83%. Our tool enables future quantitative research into the drivers, spread, prevalence and effects of Islamophobic hate speech on social media.

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在社交媒体上检测弱仇视和仇视伊斯兰的仇恨言论

摘要在社交媒体上的仇视伊斯兰的仇恨言论是当代西方政治和社会日益关注的问题。它可能对任何有针对性的受害者造成相当大的伤害，在其社区中造成恐惧和排斥，毒害公众言论并激发其他形式的极端主义和仇恨行为。因此，迫切需要自动工具来鲁棒且大规模地检测和分类仇视伊斯兰的仇恨语音，从而能够对大型文本数据集（例如从社交媒体收集的文本数据集）进行定量分析。以前的研究大多将自动检测仇恨言论作为一种二元任务。但是，伊斯兰恐惧症的性质多种多样，这对于理论上了解情况的社会科学和对社交媒体平台的有效监控通常都是不合适的。借助深入的概念性工作，我们构建了一个自动软件工具，该工具可以区分非伊斯兰憎恶，弱伊斯兰憎恶和强伊斯兰憎恶内容。准确度为77.6％，平衡准确度为83％。我们的工具可用于将来对社交媒体上仇视伊斯兰仇恨言论的动因，传播，患病率和影响进行定量研究。