Deep Learning Based Social Bot Detection on Twitter

Abstract

While social bots can be used for various good causes, they can also be utilized to manipulate people and spread malware. Therefore, it is crucial to detect bots running on social media platforms. However, social bots are increasingly successful in creating human-like messages with the recent developments in artificial intelligence. Thus, we need more sophisticated solutions to detect them. In this study, we propose a novel deep learning architecture in which three long short-term memory (LSTM) models and a fully connected layer are utilized to capture complex social media activity of humans and bots. Since our architecture involves many components connected at different levels, we explore three learning schemes to train each component effectively. In our extensive experiments, we analyze the impact of each component of our architecture on classification accuracy using four different datasets. Furthermore, we show that our proposed architecture outperforms all baselines used in our experiments.