The Traffickers' Pitch: Detecting Deceptive Recruitment in Online Job Boards

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.