HSDH: Detection of Hate Speech on social media with an effective deep neural network for code-mixed Hinglish data

The phenomenal rise of social media platforms like Twitter, Facebook, Instagram, and Reddit has led to the blending of native languages or regional tongues with English for the purpose of improving communication in linguistically open geographic regions around the world. There are many ways in which Holocaust denial can lead to an increase in violence, from direct assault to purging out of compassion. Online, people are very hostile to one another. Distinguishing between language that incites hatred and language that is disparaging is a fundamental challenge in the categorization and tracking of extremely toxic lexical features. Our research focuses on identifying harmful tweets composed in Hinglish, a fusion of Hindi and the Roman alphabet. We propose a system in this paper for classifying tweets as either abusive, neutral, or offensive. The help of Hindi-English offensive tweet dataset is comprised of tweets written in the code-transferred language of Hindi and is further subdivided into three groups: neutral, abusive, and hateful. We studied the abusive and hate speech dataset with transfer learning and pre-trained the proposed model on Hinglish-processed English tweets. With our proposed model, we were able to improve accuracy to 98.54 percent.