BotGSL: Twitter Bot Detection with Graph Structure Learning

Abstract

Abstract Twitter bot detection is an important and meaningful task. Existing methods can be bypassed by the latest bots that disguise themselves as genuine users and evade detection by mimicking them. These methods also fail to leverage the clustering tendencies of users, which is the most important feature for detecting bots at the community level. Moreover, they neglect the implicit relations between users that contain crucial clues for detection. Furthermore, the user relation graphs, which are essential for graph-based methods, may be unreliable due to noise and incompleteness in datasets. To address these issues, a bot detection framework with graph structure learning is proposed. The framework constructs a heterogeneous graph with users and their relations, extracts multiple features to characterise user intent and establishes a feature similarity graph using metric learning. Implicit relations are discovered to derive an implicit relation graph. Additionally, a semantic relation graph is generated by aggregating relation semantics among users. The graphs are then fused and embedded into a Graph Transformer for training with partially known user labels. The framework demonstrated a 91.92% average detection accuracy on three real-world benchmark, outperforming state-of-the-art methods, while also showcasing the effectiveness and necessity of each module.