Harnessing AI to Combat Hate Speech: Analyzing Machine Learning vs. Deep Learning Models

one major challenge characterizes the era of digital communication. It is controlling hate speech on the internet. Derogatory or discriminatory remarks directed at particular groups are considered hate speech. Race, gender, and religion are characteristics that set these groups apart. Social media sites made a move in the right direction. They put in place instruments for moderation. Automated systems are used by the tools. However, there are still flaws in automated systems. We investigate and contrast machine learning techniques in this work. deep learning (DL) ones as well. They are intended to identify hate speech. Conventional algorithms are assessed. These consist of Gradient Boosting, Random Forest Decision Trees, and Naive Bayes. DL algorithms also accompany them. These are BERT, CNN, and LSTM BiLSTM. AUC F1 score, recall, and precision are evaluation measures.. Emphasizing approach strengths is the aim. It also serves to highlight restrictions. Results show. Deep learning algorithms are particularly noteworthy. Specifically, BERT yields outstanding outcomes. Compared to conventional machine learning techniques, these perform better. They present the possibility of upcoming improvements. This is a crucial field of study.