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

TL;DR

The paper addresses the rising threat of job scams by shifting focus from detection of scam types to understanding how behavioral decision signals influence vulnerability and payment. It operationalizes three signals—urgency/time-pressure cues, sunk-cost influence, and social proof—into survey-based measurements and evaluates their associations with payment across scam pathways using exact inference under data sparsity, complemented by uncertainty-aware estimation and robustness checks. Key findings show urgency cues as the strongest proximal trigger for payment, sunk-cost influence as a secondary escalation mechanism, and social proof as context-dependent with weak standalone predictive power; financial vulnerability amplifies susceptibility, and missingness in responses itself conveys informative signals. The work informs human-centered design for platform-level mitigations, suggesting targeted friction near payment moments, explicit exit prompts, transparency in legitimacy cues, and improved reporting pathways, while acknowledging limitations related to sampling, measurement, and causal inference. Overall, the study contributes a principled framework for identifying critical decision points in scam trajectories and supports designing interventions that disrupt escalation before financial loss occurs, with implications for cross-platform collaboration and future longitudinal research.

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.