SOCIAL MEDIA ALGORITHMS, AI, AND MENTAL HEALTH: SOCIAL COMPARISON AND PSYCHOLOGICAL WELL-BEING – A THEORETICAL FRAMEWORK

Abstract

The present study explores the interrelationship between artificial intelligence (AI)-driven social media algorithms, social comparison behavior, and psychological well-being across different age groups. Grounded in the AI–Social Comparison–Well-Being Framework, the research examines how algorithmic exposure, social comparison tendencies, and psychological outcomes interact to influence users’ mental health. Data were collected from 250 respondents representing young, middle-aged, and older users. Results revealed that the social comparison pathway (Mean = 3.98) ranked highest, indicating its dominant role in mediating well-being outcomes, followed by the algorithmic exposure and psychological outcome pathways. Kendall’s coefficient of concordance (W = 0.168, p < 0.001) confirmed a statistically significant but moderate level of agreement among respondents. ANOVA results demonstrated significant differences across age groups for perceived social norms, anxiety and depression, algorithmic reinforcement, and personality traits. Younger participants reported higher anxiety and dependence on validation, while middle-aged users showed greater sensitivity to algorithmic influence and social norms. The findings underscore that AI-curated environments shape users’ perceptions, emotions, and social behaviors differently across age segments. The study highlights the need for algorithmic transparency, digital literacy, and psychological awareness to mitigate the negative impacts of social comparison on well-being.