Assessing Socio-Cyber Vulnerability Using Survey and Social Media Data

Abstract

The rapid growth of social media participation has increased exposure to socially engineered cyber threats (e.g., phishing, romance fraud, and tech-support scams), yet prevailing assessment tools remain fragmented: the Common Vulnerability Scoring System (CVSS) is primarily technical and largely omits human susceptibility, while the Social Vulnerability Index (SVI) is community-oriented and lacks cyber-specific modeling. To address this gap, we propose the Social Cyber Vulnerability Index (SCVI), an interpretable, uncertainty-aware metric combining two components: (i) an Individual Vulnerability Index (IVI), capturing awareness, behavior, psychological factors, and prior victimization, and (ii) an Attack Severity Index (ASI), capturing attack frequency, consequences, and sophistication. We validate SCVI across heterogeneous modalities: a nationally scoped survey (iPoll; 4,596 U.S. adults) and social-media narratives (450 Reddit r/scams reports, 2016-2024), demonstrating computation from both structured questionnaires and CI-driven feature extraction from text. Sensitivity analysis and 10,000-iteration Monte Carlo simulations show stable rankings under plausible weight variability and reveal context-dependent drivers. SCVI captures distinct socio-technical signals (Spearman correlation with CVSS $ρ= 0.33$; with SVI $ρ\approx -0.01$) and surfaces demographic and regional disparities. SCVI also provides substantially stronger separation between victim and non-victim groups than CVSS and SVI, supporting identification of high-risk populations and prioritization of interventions against emerging AI-enabled scams.