Leveraging multi-level dependency of relational sequences for social spammer detection

Much recent research has shed light on developing the relation-dependent but the content-independent framework for social spammer detection. This is mainly because the relation among users is difficult to be altered when spammers attempt to conceal their malicious intentions. Our study investigates the spammer detection problem in the context of multi-relation social networks and makes an attempt to fully exploit the sequences of heterogeneous relations for enhancing the detection accuracy. Specifically, we present the Multi-level Dependency Model (MDM). The MDM is able to exploit the user's long-term dependency hidden in their relational sequences along with short-term dependency. Moreover, MDM fully considers short-term relational sequences from the perspectives of individual-level and union level, due to the fact that the type of short-term sequences is multi-folds. Experimental results on a real-world multi-relational social network demonstrate the effectiveness of our proposed MDM on multi-relational social spammer detection. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

Recent research has focused on developing a relation-dependent but content-independent framework for social spammer detection. The Multi-level Dependency Model (MDM) aims to enhance detection accuracy by exploiting both long-term and short-term relational sequences. Experimental results demonstrate the effectiveness of MDM in detecting social spammers in multi-relational social networks.