An MCL-Based Approach for Spam Profile Detection in Online Social Networks

Over the past few years, Online Social Networks (OSNs) have emerged as cheap and popular communication and information sharing media. Huge amount of information is being shared through popular OSN sites. This aspect of sharing information to a large number of individuals with ease has attracted social spammers to exploit the network of trust for spreading spam messages to promote personal blogs, advertisements, phishing, scam and so on. In this paper, we present a Markov Clustering (MCL) based approach for the detection of spam profiles on OSNs. Our study is based on a real dataset of Facebook profiles, which includes both benign and spam profiles. We model social network using a weighted graph in which profiles are represented as nodes and their interactions as edges. The weight of an edge, connecting a pair of user profiles, is calculated as a function of their real social interactions in terms of active friends, page likes and shared URLs within the network. MCL is applied on the weighted graph to generate different clusters containing different categories of profiles. Majority voting is applied to handle the cases in which a cluster contains both spam and normal profiles. Our experimental results show that majority voting not only reduces the number of clusters to a minimum, but also increases the performance values in terms of F_P and F_B measures from F_P=0.85 and F_B=0.75 to F_P=0.88 and F_B=0.79, respectively.