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

TL;DR

This study investigates how behavioral signals like urgency and social proof can be used to identify vulnerability to job scams, using survey data and behavioral economics principles to improve early detection methods.

Contribution

It operationalizes behavioral decision signals as computational features for detecting scam vulnerability, emphasizing the role of urgency cues and measurement fidelity.

Findings

Urgency/time-pressure cues are significantly linked to payment behavior.

Opportunity-loss/FOMO cues were not reliably identified.

Emotional tone and non-response patterns vary systematically with financial loss.

Abstract

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.