INFERENCE ATTACK TWITTER USERS USING PUBLIC CLICK ANALYTICS AND TWITTER META DATA

Twitter is one of the most popular microblogging services, which is generally used to share news and updates through short messages restricted to 280 characters. However, its open nature and large user base are frequently exploited by automated spammers, content polluters, and other ill-intended users to commit various cyber crimes, such as cyberbullying, trolling, rumor dissemination, and stalking. Accordingly, a number of approaches have been proposed by researchers to Address these problems. However, most of these approaches are based on user characterization and completely disregarding mutual interactions. In this study, we present a hybrid approach for detecting automated spammers by amalgamating community based features with other feature categories, namely metadata content, and interaction-based features.