Detecting fake accounts through Generative Adversarial Network in online social media
This addresses the issue of malicious activities like identity theft for users of platforms such as Twitter, Instagram, and Facebook, but it is incremental as it builds on previous research.
The paper tackled the problem of detecting fake user accounts in online social media by proposing a method using user similarity measures and Generative Adversarial Networks (GANs) on a Twitter dataset, achieving an AUC of 80% for classification.
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.