BotCS: A Lightweight Model for Large-scale Twitter Bot Detection Comparable to GNN-based Models

Social bot detection methods using graph neural networks (GNNs) are thriving, but the structural complexity of GNN also brings more training costs on large-scale data and interpretability concerns. In this paper, we propose a social bot detection method, BotCS, which utilizes both the attribute and the structural features of the social graph at a smaller computational cost than GNN-based detection methods. BotCS makes a base prediction with a simple multilayer perceptron classifier (MLP) and then propagates the classification residuals of the training set to other nodes for further correction. Then, it smooths the corrected prediction by label propagation. With little end-to-end training, this course is low-cost and scalable. We analyze the local interaction pattern between bots and human users, and designed the corresponding residual propagation and smoothing rules from the local perspective, which ensures the interpretability of BotCS. Experimental results show that BotCS achieves similar detection results to state-of-the-art methods with one or two orders of magnitude fewer parameters.