ALGORITHMIC SOCIAL MEDIA EXPOSURE, COGNITIVE OVERLOAD, AND PSYCHOLOGICAL WELL-BEING AMONG UNIVERSITY STUDENTS IN PAKISTAN

Abstract

The rapid expansion of algorithm-driven social media platforms has reshaped how university students consume information, interact socially, and manage cognitive resources. This study examined the relationship between algorithmic social media exposure, cognitive overload, and psychological well-being among university students in Pakistan. Grounded in Cognitive Load Theory, the study proposed that algorithmic content curation increases cognitive overload, which in turn negatively affects psychological well-being. A quantitative, cross-sectional survey design was employed. Data were collected from 350 university students using validated Likert-scale instruments measuring algorithmic social media exposure, cognitive overload, and psychological well-being. The data were analyzed using descriptive statistics, correlation, regression analysis, and mediation analysis through structural equation modeling techniques. The findings revealed that algorithmic social media exposure significantly increases cognitive overload among students. Cognitive overload was found to have a strong negative effect on psychological well-being. Furthermore, algorithmic exposure directly and indirectly influenced psychological well-being, with cognitive overload acting as a significant partial mediator. The model explained a substantial proportion of variance in psychological well-being, indicating strong explanatory power. The study concludes that the psychological impact of social media is not merely driven by usage intensity but is significantly shaped by algorithmic content delivery mechanisms. The results highlight the importance of understanding cognitive mechanisms in digital media environments, particularly in developing countries where digital literacy and regulation remain limited.