Detecting Automatically Managed Accounts in Online Social Networks: Graph Embedding Approach

TL;DR

This paper presents a graph neural network-based method for detecting automated accounts in social networks, leveraging network structure and attributes to outperform traditional profile-based detection methods.

Contribution

It introduces a novel approach combining attributed and traditional graph embeddings for more effective bot detection, especially for sophisticated fake accounts.

Findings

Network structure-based methods outperform profile-based algorithms.

Graph neural networks achieve competitive detection accuracy.

The approach effectively identifies complex, human-like artificial accounts.

Abstract

The widespread of Online Social Networks and the opportunity to commercialize popular accounts have attracted a large number of automated programs, known as artificial accounts. This paper focuses on the classification of human and fake accounts on the social network, by employing several graph neural networks, to efficiently encode attributes and network graph features of the account. Our work uses both network structure and attributes to distinguish human and artificial accounts and compares attributed and traditional graph embeddings. Separating complex, human-like artificial accounts into a standalone task demonstrates significant limitations of profile-based algorithms for bot detection and shows the efficiency of network structure-based methods for detecting sophisticated bot accounts. Experiments show that our approach can achieve competitive performance compared with existing state-of-the-art bot detection systems with only network-driven features. The source code of this paper is available at: http://github.com/karpovilia/botdetection.