The DOI matches its Crossref record; none of the 8 sources its claims cite is checked for support yet.
Understanding the Impact of Social Media Algorithms on Teenagers’ Brain and Emotions: A Cross-Field Approach
Open accessAbstract
A mixed-methods study of algorithmic exposure, including personalized recommendations and autoplay, and measures of affect, distress, loneliness, sleep, and students’ reported experiences.
Overview
CoChat Summary
TL;DR
This mixed-methods study in five Delhi government schools found that higher Algorithmic Exposure Index (AEI) scores were linked to greater negative affect, psychological distress, loneliness, and sleep problems, with a marginal negative association with positive affect. AEI predicted negative affect independently of gender and total daily screen time, while qualitative themes described mechanisms such as “personalization pull,” “endless-scroll trance,” and “mood rollercoaster.”
Key findings
- AEI was positively correlated with negative affect (r = .42, p = .004), psychological distress on K10 (r = .48, p = .001), loneliness on UCLA-3 (r = .35, p = .016), and sleep problems (r = .31, p = .032); AEI had a marginal negative association with positive affect (r = -.28, p = .064).
- In multiple regression, AEI remained a unique predictor of negative affect (β = .39, p = .003) after controlling for gender and total daily screen time, with the model explaining 36% of variance (R² = .36).
- Reliability was good across scales (Cronbach’s α range .84–.90; AEI α = .86).
- Qualitative analysis produced six themes: personalization pull, endless-scroll trance, mood rollercoaster, validation loops, distorted self-image, and sleep erosion.
Methods
- Convergent mixed-methods, cross-sectional design in five Government Senior Secondary Schools in Shahdara district, Delhi; target population was Class 12 students who were regular social media users, selected by purposive sampling.
- Quantitative sample: 46 students (28 male, 18 female) completed paper-and-pen PANAS, K10, UCLA-3, Sleep Quality Index, and a study-specific AEI. Qualitative sample: 11 students (6 male, 5 female) participated in semi-structured interviews lasting 25–40 minutes.
- Quantitative analysis used Cronbach’s alpha, Pearson correlations, and multiple regression in SPSS v27; qualitative analysis used NVivo 14 and Braun & Clarke’s six-step thematic analysis.
Limitations
- The paper does not state a limitations section.
- Evident from the text: the quantitative sample is small (46 students) and the qualitative sample is very small (11 students), drawn from one district and five government schools, limiting generalizability.
- Evident from the text: the cross-sectional design cannot establish causality, and the study relies on self-report instruments plus a study-specific AEI, which may introduce reporting or measurement bias.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Verification
Claims the paper makes
0 Valid · 2 Not valid · 9 Not checked · 18 Own results and interpretations
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Pearson correlations showed significant positive associations between AEI and negative affect (r = .42,p = .004)”
- Also stated in: § RESULTS AND ANALYSIS · plain
“higher algorithmic exposure was associated with increased negative affect (r = .42,p = .004)”
§ Understanding the Impact of Social Media Algorithms on Teenagers' Brain and Emotions: A Cross-Field Approach > Abstract
plainstates an association- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § RESULTS AND ANALYSIS · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“sleep problems (r = .31,p = .032)”
- Also stated in: § RESULTS AND ANALYSIS · plain
“more reported sleep problems (r = .31,p = .032)”
§ Understanding the Impact of Social Media Algorithms on Teenagers' Brain and Emotions: A Cross-Field Approach > Abstract
plainstates an association- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § RESULTS AND ANALYSIS · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“with the model explaining 36% of the variance in negative affect (R² = .36).”
§ Understanding the Impact of Social Media Algorithms on Teenagers' Brain and Emotions: A Cross-Field Approach > RESULTS AND ANALYSIS
plainstates an association- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“loneliness (UCLA-3; r = .35,p = .016)”
- Also stated in: § RESULTS AND ANALYSIS · plain
“higher loneliness scores on the UCLA-3 scale (r = .35,p = .016)”
§ Understanding the Impact of Social Media Algorithms on Teenagers' Brain and Emotions: A Cross-Field Approach > Abstract
plainstates an association- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § RESULTS AND ANALYSIS · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“with a marginal negative association with positive affect (r = -.28,p = .064).”
- Also stated in: § RESULTS AND ANALYSIS · plain
“a marginally significant negative correlation was found between AEI and positive affect (r = -.28,p = .064)”
§ Understanding the Impact of Social Media Algorithms on Teenagers' Brain and Emotions: A Cross-Field Approach > Abstract
plainstates an association- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § RESULTS AND ANALYSIS · plain
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Citations
Cited by
Behavioral signature of the Algorithmic Self in AI-filtered digital environments
Hammad, M. Y., & Shakoor, K. (2026) · Exploration of Digital Health Technologies · doi:10.37349/edht.2026.101196
(2026) · Science and Society Insights · doi:10.65822/j.sasi/2026.004
Analysis of the Impact of "Self-Love" Content on Tiktok Social Media on the Behavior of Gen Z
Nurhikma (2026) · Mahogany Journal De Social · doi:10.37899/mjds.v3i2.343
ADOLESCENTS AND NEW FORMS OF COMMUNICATION MEDIATED BY ARTIFICIAL INTELLIGENCE
Garabet, B. I. (2026) · The International Conference "Synergies in Communication" · doi:10.24818/sic/2025/02.03
Metadata
- Authors
- Gupta, Sharma, Rawat, Sriram, Kumar, & Kumar, D. U.
- Venue
- International Journal of Environmental Sciences
- Published
- 2025-08-20
- Pages
- 553-559
- ISSN
- 2229-7359 (ISSN-L)
- OpenAlex
- W4413412089
- Semantic Scholar
- f1b3e1a4bce58f50c81b5b70b227882ba69f3ef7
- Cited by
- 4
- References
- 0
- Citations by year
- 2026: 4
- FWCI
- 3.96
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- International Journal of Environmental Sciences (publishedVersion, cc-by)
- Topics
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Social Sciences › Communication › Digital Media and Philosophy
- Social Sciences › Education › Child Development and Digital Technology
- Keywords
- social media algorithms, adolescents, negative affect, psychological distress, loneliness, sleep disorders, algorithmic personalization, social comparison, dopaminergic reward, mood dysregulation, digital hygiene
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Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Understanding the Impact of Social Media Algorithms on Teenagers’ Brain and Emotions: A Cross-Field Approach. https://app.cochat.ai/papers/10.64252/vmap3080
- The paper
- Gupta, Sharma, Rawat, Sriram, Kumar, & Kumar, D. U. (2025). Understanding the Impact of Social Media Algorithms on Teenagers’ Brain and Emotions: A Cross-Field Approach. International Journal of Environmental Sciences. https://doi.org/10.64252/vmap3080
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
