IronSense: Towards the Identification of Fake User-Profiles on Twitter Using Machine Learning

With the rampant escalation in the usage of online social media, there has been an uncurbed upsurge in the number of fake user profiles which have infiltrated social networks, and has become a formidable threat to cyber-security. It is imperative to identify such fake profiles at the earliest since such malevolent accounts are often exploited to perpetrate fraud activities, retrieve personal or confidential information from victims, spread false propaganda online or to threaten and bully victims, ensuring that their original identities remain camouflaged. Though such profiles often look realistically convincing, there exist patterns in their behavioural tendencies. In order to curb the existence of such online frauds and help users distinguish between real or possibly fake profiles on Twitter, this paper proposes the extraction of key features such as number of friends, followers, statuses, that these behavioural trends of fake and legitimate users, learnt by various machine learning algorithms can be used in predicting the nature of any Twitter accounts. The goal of our research is to implement such a machine learning model as a browser plugin or extension to facilitate convenient identification of fake users online.