Enhancing Hate Speech Detection for Social Media Moderation: A Comparative Analysis of Machine Learning Algorithms

In our ever-advancing technological age, a pressing concern emerges the challenge of preserving freedom of expression while combatting hate speech. Hate speech, in simple terms, involves hurtful messages directed at specific groups based on attributes like race, gender, and religion. Social media platforms have sought to curb hate speech through user reporting and automated content scrutiny, but their effectiveness is under scrutiny.To improve hate speech detection, we explore several machine learning algorithms, such as Naive Bayes, Random Forest, Decision Trees, and Gradient Boosting. By tweaking these algorithms and comparing their performance across metrics like AUC, CA, F1 score, Precision, and Recall, we aim to identify the best approach. Furthermore, our study reviews the current state of hate speech detection, presents the methodology used, and discusses implications for social media moderation.Our results show Random Forest as the top performer, but challenges remain. Future research may involve advanced classifiers, algorithm hybridization, and deep learning to enhance model performance in addressing the intricate landscape of online hate speech.