Published Paper Details:

People from various psychological and cultural backgrounds began communicating more directly with one another as social networks and microblogging websites grew quickly. This led to an increase in "cyber" conflicts between these individuals. As a result, hate speech is used more and more, to the point where it is starting to seriously affect these public areas. The term "hate speech" describes the use of harsh, violent, or offensive language directed towards a particular group of people who have something in common, such as their gender (sexism), race or ethnicity (racism), belief and religion, etc. Although the majority of online social networks and microblogging platforms prohibit the use of hate speech, dur to their massive scale, it is nearly impossible to master all of their material. As a result, it becomes necessary to automatically gathered from the foundation of the methodology. A machine learning algorithm is later trained using these patterns and unigrams among other features. Our tests on a test set of 2,010- tweets demonstrate that our method achieves an accuracy of 87.4 classification and 78.4 classification respectively.