# Detecting fake accounts through Generative Adversarial Network in online social media

arXiv: https://arxiv.org/abs/2210.15657 · HTML: https://mlsanity.com/paper/2210.15657/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [SMOTified-GAN for class imbalanced pattern classification problems](https://mlsanity.com/paper/2108.03235/) (2021)
- [A New Approach for Finding Cloned Profiles in Online Social Networks](https://mlsanity.com/paper/1406.7377/) (2014)

## Built on by (most cited first, 1 of 1)

- [Fake detection in imbalance dataset by Semi-supervised learning with GAN](https://mlsanity.com/paper/2212.01071/) (2022)

## Abstract (arXiv)

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.