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Social medias are increasing their influence with the vast public informationleading to their active use for marketing by the companies and organizations.Such marketing promotions are difficult to identify unlike the traditionalmedias like TV and newspaper. So, it is very much important to identify thepromoters in the social media. Although, there are active ongoing researches,existing approaches are far from solving the problem. To identify suchimposters, it is very much important to understand their strategies of socialcircle creation and dynamics of content posting. Are there any specific spammertypes? How successful are each types? We analyze these questions in the lightof social relationships in Twitter. Our analyses discover two types of spammersand their relationships with the dynamics of content posts. Our resultsdiscover novel dynamics of spamming which are intuitive and arguable. Wepropose ENWalk, a framework to detect the spammers by learning the featurerepresentations of the users in the social media. We learn the featurerepresentations using the random walks biased on the spam dynamics.Experimental results on large-scale twitter network and the correspondingtweets show the effectiveness of our approach that outperforms the existingapproaches

ENWalk: Learning Network Features for Spam Detection in Twitter