Abstract: Social Media has become a very powerful tool for information exchange as it allows users to not only consume information but also share and discuss various aspects of their interest. Nevertheless, online social platforms are beset with hateful speech - content that expresses hatred for a person or group of people. Such content can frighten, intimidate, or silence platform users, and some of it can incite other users to commit violence. Furthermore, social media gives users the freedom to express their thoughts in text without following traditional language grammars, thereby making it difficult to mine social media for insights. Despite widespread recognition of the problems posed by social media content, reliable solutions even for detecting hateful speech are lacking. The main goal of this study is to develop a reliable tool for detection of hate tweets. This paper develops an approach for detecting and classifying hateful speech that uses content produced by self-identifying hateful communities from Twitter. Results from experiments showed Naive Bayes classifier achieved significantly better performance than existing methods in hate speech detection algorithms with precision, recall, and accuracy values of 58% ,62%,and67.47%,respectively.