A Study of Hate Speech Detection Using Different Models

This paper focuses on a comparative study of algorithms that can detect social hate speech/mongering in the cyber world. The act of using online platforms to harass or bully others, known as cyberbullying, is a significant problem that has serious consequences for its victims. Hate Speech is one such issue under this category. Hate speech leads to violence, bullying, harassment and disturbances. One of the primary forms is text on social networks, affecting over a billion users. Despite the use of machine learning models, including deep learning, to tackle cyberbullying, the challenge of effectively classifying and monitoring such behavior still persists widely. To tackle this issue, an existing approach involves the use of a novel concept of CNN. The proposed approach is to understand different machine learning models, such as Logistic Regression, Naïve Bayes, Decision Tree, Random Forest and BERT. The use of NLP techniques while training the model can improve its performance in processing and analyzing textual data, the model can better understand the language used in the dataset and accurately classify instances of cyberbullying. The goal is to effectively monitor and prevent cyberbullying conducted in the form of Hate speech text, using machine learning and language processing techniques.