Spam detection in online social networks (OSNs) have become an immensely challenging task with the nature and the use of online social networks. The spammers tend to change their behaviors over time which is commonly known as the spam drift in Online Social networks. The most popular spam combatting approaches, such as classification will grew mere unsuccessful when the drift is present since old labels cannot be used to train the new classifiers. This paper presents the comprehensive results of an approach developed to identify the drift by using a set of datasets. It introduces a set of real time user interest-based features which can be used for spam drift detection in OSNs. The paper presents a hierarchical test-based drift detection approach for spam drift learning overtime, where it learns features in an unsupervised manner. The feature similarity differences, KL divergence and the Peer Acceptability of a user is then used to detect and validate the drifted content of spam users in the real time. The system automatically updates the learning model and relabeled the users with new predicted label for classification with the new knowledge acquired through drift detection. The results are encouraging. The error rate of the classifiers with drift detection is below 1%.