While substantial efforts in anti-trafficking research and practice have focused on identifying and assisting victims after exploitation occurs, comparatively less attention has been paid to preventing victimization at the recruitment stage. Although some platforms offer preventive tools, such as background checks triggered by in-person meeting detection, these measures primarily protect potential victims rather than directly limiting traffickers' recruitment activities. In this paper, we propose a computational framework to identify human trafficking recruiters through their linguistic features and to characterize their online recruitment patterns. We introduce a network-driven labeling method to construct large-scale ground truth for trafficking-at-risk job advertisements. Our results reveal significant linguistic differences between safe and risky advertisements and demonstrate that language models and embedding representations behave distinctly across these linguistic spaces. Building on these insights, we propose a multi-model ensemble classifier to improve the detection of trafficking-at-risk job ads. Finally, we analyze the geographic, gender, industry, and contact-method preferences of trafficking recruiters, revealing systematic patterns in recruitment strategies.

翻译：尽管反人口贩运研究与实践中大量工作聚焦于剥削发生后的受害者识别与救助，但在招募阶段预防受害的关注相对不足。虽然部分平台已提供预防性工具（如通过面谈检测触发的背景核查），但这些措施主要保护潜在受害者，而非直接限制贩运者的招募活动。本文提出一个计算框架，通过语言特征识别人口贩运招募者，并刻画其在线招募模式。我们引入一种网络驱动的标注方法，构建大规模“招募风险”招聘广告的真实标注数据集。研究结果显示，安全广告与风险广告存在显著的语言差异，且语言模型与嵌入表征在这些语言空间中表现出截然不同的行为特征。基于这些发现，我们提出一个多模型集成分类器，以提升对招募风险岗位广告的检测能力。最后，我们分析了贩运招募者在区域、性别、行业及联系方式选择上的偏好，揭示了招募策略的系统性模式。