Lately, there has been a growing trend in the Internet space particularly among the Online Social Media (OSMs) platforms like Twitter, Facebook etc which are becoming huge repositories of information. This information, by design, is posted by users of these websites and consequently, this information is vast, un-organized, unreliable and dynamic. It is commonly observed that along with genuine users, a lot of activity is seen from spammers or users with the intent of spreading malicious or irrelevant content. In our work, we focus on spamming activity on Twitter. Spamming activity in Twitter is can typically be reported by its users, who we refer as reporters and those who indulge in spamming activities are referred as reportees. We collected data of suspected spammers, i.e. reportees as well as of the users who reported them, i.e. reporters. Thereafter, we classified them into various categories and tried to study the ecosystem of these reportees and reporters. We used three data mining techniques i.e., decision tree, K-nearest neighbors and random forest classifier for the classification tasks. Finally, we have compared these three algorithms on the basis of their accuracy.