Social Media Bot Detection using Dropout-GAN
Anant Shukla Martin Jurecek Mark Stamp
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.