Abstract

Hate speech refers to any form of communication, whether written, spoken, or symbolic, that discriminates, threatens, or incites violence against individuals or groups based on attributes such as race, religion, ethnicity, gender, sexual orientation, or disability. Social media platforms like Twitter have become hotspots for hate speech due to their wide user base and ease of communication. The sheer volume of tweets generated every day makes it impractical to manually review and classify them for hate speech. Traditional methods for hate speech detection often rely on lexicon-based approaches, where predefined lists of offensive or discriminatory terms are used to flag potentially hateful content. However, these methods often struggle to adapt to the constantly evolving nature of hate speech and lack the context required to accurately distinguish between hate speech and other forms of expression. Given the limitations of traditional approaches, there is a need for advanced techniques that can automatically identify hate speech on Twitter. Machine learning classifiers provide a promising solution by leveraging the power of algorithms to learn patterns and features from large datasets. By using a modified TF-IDF approach, we can capture the unique characteristics of hate speech and develop a robust model capable of accurately detecting such content

Description