Malicious social bots achieve their malicious purposes by spreading misinformation and inciting social public opinion, seriously endangering social security, making their detection a critical concern. Recently, graph-based bot detection methods have achieved state-of-the-art (SOTA) performance. However, our research finds many isolated and poorly linked nodes in social networks, as shown in Fig.1, which graph-based methods cannot effectively detect. To address this problem, our research focuses on effectively utilizing node semantics and network structure to jointly detect sparsely linked nodes. Given the excellent performance of language models (LMs) in natural language understanding (NLU), we propose a novel social bot detection framework LGB, which consists of two main components: language model (LM) and graph neural network (GNN). Specifically, the social account information is first extracted into unified user textual sequences, which is then used to perform supervised fine-tuning (SFT) of the language model to improve its ability to understand social account semantics. Next, the semantically enriched node representation is fed into the pre-trained GNN to further enhance the node representation by aggregating information from neighbors. Finally, LGB fuses the information from both modalities to improve the detection performance of sparsely linked nodes. Extensive experiments on two real-world datasets demonstrate that LGB consistently outperforms state-of-the-art baseline models by up to 10.95%. LGB is already online: https://botdetection.aminer.cn/robotmain.

翻译：恶意社交机器人通过传播虚假信息、煽动社会舆论实现其恶意目的，严重危害社会安全，使其检测成为关键问题。近期，基于图的机器人检测方法已取得最先进的性能。然而，我们的研究发现社交网络中存在大量孤立及连接稀疏的节点（如图1所示），基于图的方法难以有效检测此类节点。为解决该问题，本研究聚焦于有效利用节点语义与网络结构以联合检测稀疏连接节点。鉴于语言模型在自然语言理解方面的卓越性能，我们提出一种新颖的社交机器人检测框架LGB，该框架由两大核心组件构成：语言模型与图神经网络。具体而言，首先将社交账户信息提取为统一的用户文本序列，随后通过监督微调语言模型以提升其理解社交账户语义的能力。接着，将语义增强的节点表示输入预训练的图神经网络，通过聚合邻居信息进一步优化节点表征。最终，LGB融合来自两种模态的信息以提升稀疏连接节点的检测性能。在两个真实数据集上的大量实验表明，LGB始终优于最先进的基线模型，最高提升幅度达10.95%。LGB系统已上线运行：https://botdetection.aminer.cn/robotmain。