Social bots significantly threaten online public opinion through manipulation and misinformation, posing detection challenges due to high anthropomorphism and concealment. GNN methods show superior performance but face deployment hurdles on real-world platforms because of their reliance on multi-hop neighbor information during inference. Conversely, pure text-based methods lack collective behavior modeling and robustness against advanced bots. This paper proposes LKD, a social bot detection framework for graph-less deployment. The framework utilizes large language models to summarize historical tweets, compressing long-text information to construct multi-source inputs including metadata, profiles, and tweets. By employing a GNN as the teacher and a pre-trained LM as the student, LKD transfers structural knowledge to a text-based model via dual-objective knowledge distillation across prediction distributions and feature spaces. Experiments on Cresci-2015 and TwiBot-20 datasets show that the graph-less LKD-LM mode outperforms state-of-the-art methods in accuracy and F1-score. It maintains stable performance in label-scarce and sparse-graph scenarios, providing an efficient, robust solution for social media platforms with restricted interfaces or real-time requirements.
Ye et al. (Sat,) studied this question.