Modeling Behavioral Signals in Job Scams: A Human-Centered Security Study

Job scams have emerged as a rapidly growing form of cybercrime that manipulates human decision-making processes. Existing countermeasures primarily focus on scam typologies or post-loss indicators, offering limited support for early-stage intervention. In this study, we examine how behavioral decision signals can be operationalized as computational features for identifying vulnerability-associated signals in job fraud. Using anonymous survey data collected from a university population, we analyze two dominant job scam pathways: payment-based scams that require upfront fees and task-based scams that begin with small rewards before escalating to financial demands. Drawing on behavioral economics, we operationalize sunk cost influence, urgency/time-pressure cues, and social proof as measurable behavioral signals, and analyze their association with payment behavior using exact inference under sparsity and uncertainty-aware estimation, with social proof treated as a context-dependent legitimacy cue rather than a standalone predictor. Our results show that urgency/time-pressure cues are significantly associated with payment behavior, consistent with their role as proximal compliance triggers during escalation. In contrast, opportunity-loss/FOMO cues were not reliably identifiable under the current operationalization in our encounter subset, highlighting the importance of measurement fidelity and cue-definition consistency. We further observe that emotional tone in victim narratives and selective non-response to sensitive questions vary systematically with financial loss and reporting behavior, suggesting that missingness may reflect a combination of survey fatigue and selective non-disclosure for sensitive items rather than purely random noise.