Social media bot detection using Dropout-GAN
Publication Date
1-1-2024
Document Type
Article
Publication Title
Journal of Computer Virology and Hacking Techniques
DOI
10.1007/s11416-024-00521-5
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. In terms of classification accuracy, our approach outperforms 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.
Department
Computer Science
Recommended Citation
Anant Shukla, Martin Jureček, and Mark Stamp. "Social media bot detection using Dropout-GAN" Journal of Computer Virology and Hacking Techniques (2024). https://doi.org/10.1007/s11416-024-00521-5
