Social Bot Detector using Graph Neural Networks

Abstract

The importance of online social networks (OSNs) has been fueled by the human need for digital communication and broadcasting, as well as the improved state of internet connections and electronic devices. Meanwhile, social bots have been designed to automatically replicate the behavior of legitimate users in order to manipulate these OSNs. As a result, social bot detectors have been conducted concurrently, mostly on Twitter, in an attempt to discover new strategies for countering social bot attacks. In this paper, we propose SOBOG, a deep learning architecture that takes tweet relations, tweet semantics, and user properties into account to perform account-level and tweet-level detection. SOBOG also achieves outstanding performance on both tasks.