Detecting and Mitigating the Effect of Manipulated Reputation on Online Social Networks.

In recent times, online social networks (OSNs) are being used not only to communicate but to also create a public/social image. Artists, celebrities and even common people are using social networks to build their brand value and gain more visibility either amongst a restricted set of people or public. In order to enable user to connect to other users in the OSN and gain following and appreciation from them, various OSNs provide different social metrics to the user such as Facebook likes, Twitter followers and Tumblr reblogs. Hence, these metrics give a sense of social reputation to the OSN user. As more users are trying to leverage social media to create a brand value and become more influential, spammers are luring such users to help manipulate their social reputation with the help of paid service (black markets) or collusion networks. In this work, we aim to build a robust alternate social reputation system and detect users with manipulated social reputation. In order to do so, we first start by understanding the underlying structure of various sources of crowdsourced social reputation manipulation like blackmarkets, supply-driven microtask websites and collusion networks. We then build a mechanism for an early detection of users with manipulated social reputation. Our initial results are encouraging and substantiate the possibility of a robust social reputation system.