Impact of Social Media Platforms on Physical Characteristics and Psychological Profiles
Bibliographic record
Abstract
This study explores the complex relationships between social media platform usage and psychological outcomes, focusing on how different platforms impact users' mental health. Data was collected from 6,104 participants across a variety of platforms, including Instagram, Snapchat, TikTok, Facebook, LinkedIn, Twitter, and YouTube. The analysis focused on key psychological variables, including anxiety (STAI1), perceived stress (PSS4), psychological flexibility (AAQ1), and loneliness (UCLA1). Our findings show that visually driven platforms like Instagram, Snapchat, and TikTok are associated with significantly higher levels of anxiety, perceived stress, and psychological rigidity. In contrast, platforms such as Facebook and LinkedIn, which prioritize personal relationships or professional networking, were linked to lower levels of psychological distress. Snapchat users reported the highest anxiety scores (16.39 ± 2.50), while Facebook users exhibited the lowest anxiety levels (13.71 ± 4.17), indicating platform-specific differences in psychological outcomes. Additionally, the study found that YouTube users experienced the highest levels of perceived stress (2.27 ± 1.23), followed by Snapchat (2.11 ± 1.01), while Facebook users reported the lowest stress levels (1.76 ± 1.26). These results highlight the psychological risks associated with visually focused platforms, which encourage social comparison and can exacerbate anxiety and stress. The findings suggest that specific platforms may contribute to negative psychological outcomes, potentially influenced by their emphasis on appearance and curated content. The study also points to the need for targeted interventions, such as digital literacy programs and mental health resources, to mitigate these risks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".