Leveraging Multi-level Dependency of Relational Sequences for Social Spammer Detection

TL;DR

This paper introduces the Multi-level Dependency Model (MDM) that leverages long-term and short-term relational sequences in multi-relation social networks to improve social spammer detection accuracy.

Contribution

The study proposes a novel MDM framework that exploits multi-level dependencies in heterogeneous relational sequences for enhanced spammer detection.

Findings

MDM outperforms existing methods on real-world datasets.

Exploiting multi-level dependencies improves detection accuracy.

Considering individual and union-level short-term sequences is effective.

Abstract

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.