Online environments, including email and social media platforms, are continuously threatened by malicious content designed by attackers to install malware on unsuspecting users and/or phish them into revealing sensitive data about themselves. Often slipping past technical mitigations (e.g. spam filters), attacks target the human element and seek to elicit trust as a means of achieving their nefarious ends. Victimized end-users lack the discernment, visual acuity, training, and/or experience to correctly identify the nefarious antecedents of trust that should prompt suspicion. Existing literature has explored trust, trust-propensity, and victimization, but studies lack data capture richness, realism, and/or the ability to investigate active user interactions. This paper defines a data collection and fusion approach alongside new open-sourced behavioral analysis tooling that addresses all three factors to provide researchers with empirical, evidence-based, insights into active end-user trust behaviors. The approach is evaluated in terms of comparative analysis, run-time performance, and fused data accuracy.