Hateful Text Detection Using Comparative Analysis of Machine Learning Algorithm

Abstract

Social media platforms' explosive expansion has increased online interactions and made it easier to share information and communicate with others. But this accessibility has also made cruel and abusive speech easier to propagate, endangering both people and communities. For internet platforms, hate speech—offensive language directed at people or groups because of traits like race, ethnicity, gender, religion, or nationality—presents a serious problem. Conventional moderation methods are unable to keep up with the sheer volume and dynamic nature of hate speech. In order to efficiently detect and categorize hate speech, this project intends to create a Hateful Speech Detection System utilizing machine learning and natural language processing (NLP) approaches. To improve detection accuracy, our model adds more contextual information from the Web, in contrast to traditional methods that only use textual data. Utilizing supervised learning algorithms like Random Forest and Decision Tree, in addition to feature extraction methods like TF-IDF and word embeddings, the system enhances its capacity to distinguish between speech that is hateful and speech that is not. The suggested approach tackles important issues such linguistic ambiguity, context reliance, and the development of hate speech patterns. It also takes into account moral issues with bias, free expression, and false positives, guaranteeing a fair approach to content management. In addition to reducing the dissemination of damaging content, the system promotes a more secure and welcoming online community. This study advances NLP and AI ethics by including AI-driven hate speech identification, hence bolstering proactive moderation tactics for online platforms.