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The rise of algorithm-driven social media platforms has intensified the visibility andnormalization of Naira abuse content in Nigeria. This study investigates how social mediaalgorithms shape youth engagement with Naira abuse content among 400 undergraduate studentsof Alex Ekwueme Federal University, Ndufu Alike. Anchored on Algorithmic Media Theory andSocial Learning Theory, the study examines how algorithmic recommendation systems amplifysensational currency-related content and how repeated exposure fosters imitation and behaviouralnormalization among youths. Using a quantitative survey design, the study measured algorithmicexposure, celebrity influence, and youth engagement. Reliability analysis showed strong internalconsistency (Cronbach’s α = .89), while Exploratory Factor Analysis identified three latentconstructs: Algorithmic Exposure, Celebrity Influence, and Youth Engagement. All items met theretention criteria (factor loadings ≥ .50) and were included in the final analysis; only thehighest-loading items are displayed in the results table for brevity. Correlation analysis revealed astrong positive association between algorithmic exposure and youth engagement (r = .62, p < .001).Hierarchical regression further showed that algorithmic exposure significantly predicted youthengagement (β = .54, p < .001), even after controlling for demographics and celebrity influence.Findings demonstrate that algorithms not only amplify visually stimulating Naira abuse content butalso reinforce behavioural modelling processes described in Social Learning Theory, wherebyyouths imitate repeated, high-status behaviours displayed by celebrities. The study concludes thatalgorithmic amplification and observational learning jointly normalize harmful currency-relatedpractices. It recommends enhanced algorithmic transparency, stronger regulatory collaboration, andyouth-focused digital literacy interventions to mitigate the spread and influence of Naira abusecontent.