key task for automatic hate-speech detection on social media is the separation of hate speech from different instances of offensive language. Lexical detection strategies tend to have low precision due to the fact they classify all messages containing precise terms as hate speech and previous work the use of supervised gaining knowledge of has failed to differentiate among the two classes. We used a crowd-sourced hate speech lexicon to acquire tweets containing hate speech keywords. We use crowdsourcing to label a pattern of those tweets into three classes: those containing hate speech, only offensive language, and those with neither. We educate a multi-magnificence classifier to distinguish among those one-of-a-kind categories. near analysis of the predictions and the errors suggests when we can reliably separate hate speech from different offensive language and while this differentiation is extra difficult. we discover that racist and homophobic tweets are much more likely to be categorized as hate speech but that sexist tweets are normally labeled as offensive. Tweets without specific hate key phrases also are more difficult to categories.