Anant Shukla

Publication Date

Spring 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)


Computer Science

First Advisor

Mark Stamp

Second Advisor

Katerina Potika

Third Advisor

Thomas Austin


Generative Adversarial Network, Social Media, Random Forest, Multilayer Perceptron, k-Nearest Neighbors, Support Vector Machine


Bot activity on social media platforms is a pervasive problem, undermining the credibility of online discourse and potentially leading to cybercrime. We propose an approach to bot detection using Generative Adversarial Networks (GAN). We discuss how we overcome the issue of mode collapse by utilizing multiple discriminators to train against one generator, while decoupling the discriminator to perform social media bot detection and utilizing the generator for data augmentation. We demonstrate that our approach outperforms---in terms of accuracy---the state-of-the-art techniques in this field. We also show how the generator in the GAN can be used to evade such a classification technique.

Available for download on Thursday, May 15, 2025