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The study aims to explore how algorithmically personalized social media content affects mental health and political polarization.

June 13, 2026Open Access

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

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Mixed-methods study examines algorithmic content's impact on mental health and political beliefs, indicating significant effects.

The study aims to explore how algorithmically personalized social media content affects mental health and political polarization.
Mixed-methods approach with N = 210 social media users aged 18–45
Utilized standardized psychological assessments (GAD-7, PHQ-9, Rosenberg Self-Esteem Scale) and sentiment analysis
Conducted correlation and regression analyses to examine relationships between variables
Significant positive correlation found between algorithmic content exposure and anxiety (r = 0.61, p < 0.01)
Significant positive correlation found between algorithmic content exposure and depression (r = 0.54, p < 0.01)
Exposure to ideologically uniform content significantly predicted political polarization (β = 0.47, p < 0.01)

Dr. Hidayatulla Kamaruddin Pirjade (2026) studied this question.

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