Detecting fake accounts through Generative Adversarial Network in online social media

TL;DR

This paper introduces a novel GAN-based approach utilizing user similarity metrics to detect fake accounts on social media platforms, achieving an 80% AUC in Twitter data classification.

Contribution

It presents a new method combining user similarity and GANs for fake account detection, advancing previous anomaly detection techniques.

Findings

Achieved 80% AUC in fake account detection

Utilized user similarity measures with GANs

Enhanced understanding of social network anomalies

Abstract

Online social media is integral to human life, facilitating messaging, information sharing, and confidential communication while preserving privacy. Platforms like Twitter, Instagram, and Facebook exemplify this phenomenon. However, users face challenges due to network anomalies, often stemming from malicious activities such as identity theft for financial gain or harm. This paper proposes a novel method using user similarity measures and the Generative Adversarial Network (GAN) algorithm to identify fake user accounts in the Twitter dataset. Despite the problem's complexity, the method achieves an AUC rate of 80\% in classifying and detecting fake accounts. Notably, the study builds on previous research, highlighting advancements and insights into the evolving landscape of anomaly detection in online social networks.