Analysis of Machine Learning Models for Hate Speech Detection in Online Content

The internet has become a vital platform for people to express their views and beliefs. The users on social media platforms and blogging services are free to publish anything they like. But occasionally, information that targets a particular group of people intending to promote hate or discrimination rises causing trouble in the community. We refer to such material as hate speech. Hate speech has the potential to significantly damage social peace and harmony. Extremism and societal instability have occasionally resulted from hate speech. The several forms of hate speech like racism, sexism, hate speech based on religion, etc.—as well as the approaches put out to combat them are covered. Additionally, we list the problems and provide fixes for issues with hate speech identification on the open internet. Therefore, it is necessary to monitor hate speech on the internet. We analyze relevant research in the field of hate speech detection in this paper. Our proposed system not only identify the Hate Speech on internet but also label them into categories like (Offensive Speech, Hate Speech, fair Speech etc.) The gathered information can be processed to provide Hate speech reports, which will make the internet more user-friendly for anyone using it.