The Impact of Algorithm-Driven Social Media Content on Mental Health and Political Polarization: A Mixed-Methods Empirical Study

Abstraction:The rapid growth of algorithm-based social media platforms has profoundly altered the ways inwhich information is disseminated, emotional responses are experienced, and politicalparticipation occurs. This research examines the connection between the personalization ofalgorithmic content, mental health effects, and political division. A mixed-methods strategywas utilized, gathering data from N = 210 social media users aged 18–45 through structuredsurveys and sentiment analysis of interaction patterns. Standardized psychologicalassessments, including GAD-7 (anxiety), PHQ-9 (depression), and the Rosenberg Self-EsteemScale, were utilized. Correlation and regression analyses indicated a significant positivecorrelation between exposure to algorithmic content and anxiety (r = 0.61, p < 0.01) as well asdepression (r = 0.54, p < 0.01). Additionally, exposure to ideologically uniform content was asignificant predictor of political polarization (β = 0.47, p < 0.01). The results illustrate thatalgorithmic personalization plays a role in emotional distress and ideological division. Thisstudy underscores the necessity for ethical AI frameworks, transparent governance ofalgorithms, and initiatives aimed at enhancing digital literacy to promote healthier digitalenvironments.