Enhancing Sinhala Hate Speech Detection in Online Platforms

Abstract

The rise of deep learning methodologies has indeed revolutionized text analysis, enabling more sophisticated and nuanced understanding of language dynamics. With the proliferation of social media platforms, these advancements have been particularly crucial in navigating the vast amounts of data generated by online interactions. However, amidst the benefits of this digital age, the prevalence of hate speech has emerged as a pressing concern, transcending linguistic and cultural boundaries. In the context of Sinhala, a language rich in nuances and deeply intertwined with cultural complexities, the challenges in detecting and mitigating hate speech are further compounded. Language is not merely a tool for communication but also a reflection of societal norms, values, and power structures. In the Sinhala-speaking context, historical legacies, religious beliefs, and political tensions intertwine to shape discourse in multifaceted ways. Consequently, any hate speech detection mechanism must navigate these intricate layers of meaning, accounting for cultural sensitivities and contextual nuances to ensure accurate identification of harmful content. The integration of deep learning techniques and advanced semantic analysis holds promise in enhancing hate speech detection in Sinhala. By leveraging the power of neural networks to discern patterns and contexts within textual data, such mechanisms can offer a more nuanced understanding of language dynamics. Moreover, the evaluation of these tools on real-world social media data not only validates their effectiveness but also provides insights into the evolving nature of online discourse. Ultimately, addressing hate speech in Sinhala and similar low-resource languages requires a multifaceted approach that combines technological innovation with cultural sensitivity and community engagement to foster safer and more inclusive online spaces.