This paper describes the accuracy of various algorithms for classification of text on the basis of gender identification. We examined the knowledge extracted from corpus of Twitter's online social media in term of gender identity. By comparing algorithms on different feature sets, we established a feature set of 20 distinct arguments which increase the correctness of gender identification on all over the twitter. We reported accuracies of three algorithms obtained by using two approaches applied on two classes of gender i.e. male and female; a model where a lot of features are reduced using powerset transformation.