Beyond Binary: Revealing Variations in Islamophobic Content with Hierarchical Multi-Class Classification

Abstract

In the digital age, the rise of Islamophobia-marked by an irrational fear or discrimination against Islam and Muslims-has emerged as a pressing issue, especially on social media platforms. In this paper we employs a multi-class classification system, moving beyond traditional binary models. We categorize Islamophobic content into three main classes and various subclasses, covering a range from subtle biases to explicit incitement. Comparative analysis of data from Reddit and Twitter illuminates the distinct prevalence and types of Islamophobic content specific to each platform. This paper deepens our understanding of digital Islamophobia and provides insights for crafting targeted online counter strategies. Additionally, it highlights the role of machine
and deep learning in detecting and addressing Islamophobic content, emphasizing their significance in resolving complex social issues in the digital sphere.