Social Media Surveillance: Detecting and Mitigating Islamophobic Hate

Abstract

Islamophobic hate speech on social media has become a pressing global concern, contributing to discrimination, harassment, and even violence against Muslim communities. This research aims to develop a robust and effective approach to detect and mitigate such harmful content. By leveraging advanced natural language processing techniques and machine learning algorithms, this study proposes a comprehensive framework that can accurately identify Islamophobic hate speech within large-scale social media datasets. Furthermore, the research explores strategies for mitigating the spread of Islamophobic content, including automated flagging systems, user education programs, and community-driven initiatives. The findings of this study contribute to a deeper understanding of the prevalence and impact of Islamophobic hate speech on social media platforms and provide valuable insights for policymakers, technology developers, and civil society organizations in their efforts to create more inclusive and respectful online environments.