  Much recent research has shed light on the development of the
relation-dependent but content-independent framework for social spammer
detection. This is largely because the relation among users is difficult to be
altered when spammers attempt to conceal their malicious intents. 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
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.
