Proactive Measures: Using Machine Learning to Anticipate and Combat Hate Speech on Twitter

This paper explores using machine learning (ML) to detect hate speech on Twitter. It collects a large dataset, preprocesses tweets, and extracts features. Various ML algorithms are trained and evaluated, including SVM, Naive Bayes, Logistic Regression, and Neural Networks. The study also investigates the impact of different features and explores techniques like word embeddings and deep learning. Results provide insights into effective methods for hate speech detection, crucial for combating online toxicity.