Understanding the Impact of Social Media Algorithms on Teenagers’ Brain and Emotions: A Cross-Field Approach

Abstract

This study tries to understand the impact of social media algorithms on teenagers’ brain and emotions through a cross-field approach that integrates concepts from cognitive neuroscience, behavioral psychology, and computational social science. Set in five government senior secondary schools of Shahdara district, Delhi, the research employed a mixed-methods design to capture both the measurable and lived dimensions of algorithmic curation. Quantitatively, data were collected from 46 Class 12 students (28 male, 18 female) using standardized tools to assess affect, distress, sleep quality, and loneliness, alongside a study-specific Algorithmic Exposure Index (AEI) reflecting the intensity of personalized recommendations, autoplay features, and time spent on algorithm-driven feeds. Qualitatively, in-depth interviews were conducted with 11 students (6 male, 5 female) to probe perceived changes in attention, mood volatility, reward sensitivity (likes/shares), body image, social comparison, and sleep hygiene. Reliability analyses demonstrated good internal consistency (Cronbach’s α range: .84–.90 across scales; AEI α = .86). Pearson correlations showed significant positive associations between AEI and negative affect (r = .42, p = .004), psychological distress (K10; r = .48, p = .001), loneliness (UCLA-3; r = .35, p = .016), and sleep problems (r = .31, p = .032), with a marginal negative association with positive affect (r = −.28, p = .064). A multiple regression model (R² = .36) indicated AEI as a unique predictor of negative affect (β = .39, p = .003) after controlling for gender and total daily screen time. Thematic analysis (six themes) illuminated mechanisms of “personalization pull,” “endless-scroll trance,” and “mood rollercoaster,” among others, consistent with dopaminergic reward loops and social comparison processes. Findings suggest that algorithmically intensified feeds are linked with heightened distress and mood dysregulation in late adolescents, implying a need for school-level digital hygiene programs, transparent recommender policies, and parental co-regulation strategies. The study concludes that a cross-field lens offers a more complete map of how algorithmic design meets developing adolescent neuro-emotional systems, and it outlines actionable pathways for educators and policymakers in urban Indian contexts.