Towards Safer Online Spaces: Deep Learning for Hate Speech Detection in Code-Mixed Social Media Conversations

Abstract

In the midst of the widespread adoption of technology, particularly among younger generations, the increasing prevalence of hate speech online has become a pressing global concern. This research paper aims to address this urgent issue by conducting a thorough investigation into hate speech detection in Hindi-English code-mixed data. Existing research has largely approached hate speech recognition as a text classification problem, focusing on predicting the class of a message based solely on its textual content. Our task, however, delves into the classification of hateful content disseminated through tweets, comments, and replies on Twitter, taking into account the contextual intricacies inherent in social media communication. In this context, contextual nuances play a crucial role in understanding communication dynamics. By employing state-of-The-Art deep learning techniques tailored to the unique linguistic characteristics of each language, this research makes a significant contribution to the development of robust and culturally sensitive hate speech detection systems. Such systems are essential for creating safer online environments and promoting cross-cultural understanding. Warning: The content of this paper may contain offensive material, reader discretion is advised. © 2024 ACM.