DECIFE: Detecting Collusive Users Involved in Blackmarket Following Services on Twitter

TL;DR

DECIFE is a novel framework that detects collusive users involved in blackmarket following services on Twitter by analyzing user relationships and linguistic features through a sophisticated graph-based learning approach.

Contribution

This paper introduces DECIFE, the first method to identify collusive users in blackmarket following services using a heterogeneous network and attention-based graph learning.

Findings

DECIFE outperforms existing methods in detection accuracy.

The framework effectively captures semantic relations between users.

Experimental results validate DECIFE's robustness and effectiveness.

Abstract

The popularity of Twitter has fostered the emergence of various fraudulent user activities - one such activity is to artificially bolster the social reputation of Twitter profiles by gaining a large number of followers within a short time span. Many users want to gain followers to increase the visibility and reach of their profiles to wide audiences. This has provoked several blackmarket services to garner huge attention by providing artificial followers via the network of agreeable and compromised accounts in a collusive manner. Their activity is difficult to detect as the blackmarket services shape their behavior in such a way that users who are part of these services disguise themselves as genuine users. In this paper, we propose DECIFE, a framework to detect collusive users involved in producing 'following' activities through blackmarket services with the intention to gain collusive followers in return. We first construct a heterogeneous user-tweet-topic network to leverage the follower/followee relationships and linguistic properties of a user. The heterogeneous network is then decomposed to form four different subgraphs that capture the semantic relations between the users. An attention-based subgraph aggregation network is proposed to learn and combine the node representations from each subgraph. The combined representation is finally passed on to a hypersphere learning objective to detect collusive users. Comprehensive experiments on our curated dataset are conducted to validate the effectiveness of DECIFE by comparing it with other state-of-the-art approaches. To our knowledge, this is the first attempt to detect collusive users involved in blackmarket 'following services' on Twitter.