Social Data Analysis: Cyber Recruitment Analysis Spam Detection over Twitter Dataset Using SVM & ARIMA Model

Social media has emerged as a vital mode of communication to friends as well as communities. Many communities have their presence on social media, as they provide non restricted communication with fewer authentications thus becomes the target for identity deception. Cyber recruitment is one of the spammer activities which is also being performed using the open platform social media. Thus analysis of data exchange and filtering them based on communication, alarming the network is the requirement. Cyber posts analysis is previously done by many approaches such as text filtering, key bag word analysis, pattern analysis etc. they are limited with their dataset size and requirement of pre-learning by programmers. Thus the requirement of an automated approach with auto learning of pattern with proper detection is needed. In this paper an Enhanced SVM CTM based approach is proposed, this approach gives the analysis of data taken from twitter posts. Performing the pre-process through NLP and further applying the SVM CTM approach over filtered data for spam analysis. Comparison analysis with traditional approaches shows the better prediction ratio