Twitter currently is one of the leading social networks worldwide based on the amount of monthly active users after Facebook and Instagram. People uses Twitter mostly to find out more information about breaking news or keeping up with news in general by following trending topics. As Twitter become a source of news breaks contents in form of comments and replies to share the newest ideas. Therefore, several mobile applications that utilize Twitter API has been developed to provide a convenient way in providing trending topics to their user. Twitter trending topics offers an effective opportunity in marketing point of view for online marketers to promote their marketing contents. Spam contents in Twitter were found to be distracting and annoying for certain users, thus mobile application to deliver spam-free Twitter trending topics contents is needed. This research designs an Android application framework that allow developers to build their own implementation of spam detection classifier for Twitter contents as application library. This research implements two classification methods, i.e. Naïve Bayes and K-Nearest Neighbor, to identify spam in Twitter trending topics. The Naïve Bayes and K-Nearest Neighbor classification methods are able to detect spam and ham contents with 82% and 71% accuracy respectively.