Large Language Model-driven (LLM-driven) social bots pose a growing threat to online discourse by generating human-like content that evades conventional detection. Existing methods suffer from limited detection accuracy due to overreliance on single-modality signals, insufficient sensitivity to the specific generative patterns of Artificial Intelligence-Generated Content (AIGC), and a failure to adequately model the interplay between linguistic patterns and behavioral dynamics. To address these limitations, we propose TRACE-Bot, a unified dual-channel framework that jointly models implicit semantic representations and AIGC-enhanced behavioral patterns. TRACE-Bot constructs fine-grained representations from heterogeneous sources, including personal information data, interaction behavior data and tweet data. A dual-channel architecture captures linguistic representations via a pretrained language model and behavioral irregularities via multidimensional activity features augmented with signals from state-of-the-art (SOTA) AIGC detectors. The fused representations are then classified through a lightweight prediction head. Experiments on two public LLM-driven social bot datasets demonstrate SOTA performance, achieving accuracies of 98.46% and 97.50%, respectively. The results further indicate strong robustness against advanced bot strategies, highlighting the effectiveness of jointly leveraging implicit semantic representations and AIGC-enhanced behavioral patterns for emerging LLM-driven social bot detection.

翻译：大语言模型驱动的社交机器人通过生成类人内容逃避传统检测，对在线话语构成日益严重的威胁。现有方法由于过度依赖单模态信号、对人工智能生成内容（AIGC）特定生成模式的敏感性不足，以及未能充分建模语言模式与行为动态之间的交互，导致检测精度受限。为解决上述局限，我们提出TRACE-Bot——一种统一的双通道框架，联合建模隐式语义表征与AIGC增强行为模式。TRACE-Bot从异构源（包括个人信息数据、交互行为数据和推文数据）构建细粒度表征。其双通道架构通过预训练语言模型捕获语言表征，并利用多维活动特征（经最新AIGC检测器信号增强）捕捉行为异常。融合后的表征通过轻量级预测头进行分类。在两个公开LLM驱动社交机器人数据集上的实验表明，该方法实现了98.46%和97.50%的准确率，达到当前最优性能。结果进一步显示该方法对先进机器人策略具有强鲁棒性，凸显了联合利用隐式语义表征与AIGC增强行为模式进行新兴LLM驱动社交机器人检测的有效性。