Publication Date
Spring 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Mark Stamp
Second Advisor
Katerina Potika
Third Advisor
Thomas Austin
Keywords
Generative Adversarial Network, Social Media, Random Forest, Multilayer Perceptron, k-Nearest Neighbors, Support Vector Machine
Abstract
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.
Recommended Citation
Shukla, Anant, "Social Media Bot Detection using Dropout-GAN" (2024). Master's Projects. 1344.
DOI: https://doi.org/10.31979/etd.j2wj-qnv8
https://scholarworks.sjsu.edu/etd_projects/1344
