Behavioral Analysis of Users for Spammer Detection in a Multiplex Social Network.

There are now a growing number of social networking websites with millions of users, creating a fertile ground for “spammers” to abuse opportunities in these websites for their own gain through constant exposure of malicious communications to other users. The variety of interactions afforded by these social networks has resulted in a Multiplex Network of interactions. In these networks, malicious users evade detection by frequently changing the nature of their activities. This makes it challenging to analyse users’ interactions to capture anomalous behaviours. In this paper, we aimed to detect spammers in a large time-evolving multiplex social network called Tagged.com. For this purpose, we used four different sets of features: (i) a set of light-weight behavioural features to capture the structural behaviour of users in their neighbourhood network; (ii) a set of bursty features and (iii) sequence-based features for capturing the temporal behaviour of users; and (iv) a set of profile-based features which was used as a side information. In addition, we also employed an unsupervised Laplacian Score based approach for feature selection and space dimensionality reduction. The experimental results showed an accuracy of over 88% in spammer detection with a lower empirical time complexity for feature extraction. Implementing behavioural and bursty features in a relational data management system makes the proposed approach more practical since most of the real-world networks store their data in relational databases.