The increasing use of social media and information sharing has given major benefits to humanity. However, this has also given rise to a variety of challenges including the spreading and sharing of hate speech messages. Thus, to solve this emerging issue in social media sites, recent studies employed a variety of feature engineering techniques and machine learning algorithms to automatically detect the hate speech messages on different datasets. However, to the best of my knowledge, not much research has been done to compare the variety of machine learning algorithms to evaluate which machine learning algorithm outshine on a standard publicly available dataset. Hence, the aim of this paper is to compare the performance of machine learning algorithms to appraise their performance on a publicly available dataset having three distinct classes. The study has proved that the bigram features when used with the support vector machine algorithm best performed with 79% off overall accuracy. My study holds practical implication and can be used as a baseline study in the area of detecting automatic hate speech messages.