Necessary Condition Analysis of Social Comparison, Envy, and Gullibility in College Students Exposed to Algorithmically Curated Media

College students using algorithmically curated media encounter personalized feeds, popularity cues, socially salient comparison targets, and rapidly circulating claims. This study presents a reproducible protocol for assessing whether algorithmic curation exposure, upward social comparison, and envy exhibit necessary lower-bound relationships with high gullibility. The protocol was implemented in a completed cross-sectional primary study. Adult college students completed an anonymous online questionnaire measuring algorithmic curation exposure, upward social comparison, benign and malicious envy, gullibility self-report, media literacy, sharing habit, and perceived accuracy and sharing intention for six true and six false but non-harmful headlines. Gullibility self-report, false-headline acceptance, and false-headline sharing intention were combined into an equally weighted multidomain index representing dispositional susceptibility, acceptance of inaccurate claims, and dissemination intention. Predefined quality-control procedures addressed rapid completion, failed attention checks, straight-line responding, excessive missingness, and duplicate participation before construct scoring. Necessary condition analysis evaluated whether minimum levels of candidate conditions constrained attainment of high composite gullibility, whereas complementary regression estimated adjusted average associations. Bottleneck analysis retained the original 1-6 measurement metric to quantify minimum observed condition levels across increasing outcome targets. The completed primary study retained 374 participants after predefined quality-control screening. Findings were interpreted as necessary-but-not-sufficient lower-bound patterns and adjusted associations within the cross-sectional design. The protocol provides a reproducible framework for studying comparison-related affect and susceptibility to online claims in algorithmically curated media environments.