Social Media Bot Detection using Dropout-GAN
This addresses the problem of bot activity undermining online credibility for social media platforms, but it is incremental as it builds on existing GAN methods.
The paper tackles bot detection on social media by proposing a GAN-based method that uses multiple discriminators to prevent mode collapse, achieving higher classification accuracy than state-of-the-art techniques, and demonstrates the generator can evade such detection.
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.