A Study on the Relationship Between Social Media Platform Features and Young Women’s Appearance Satisfaction: A Multi-theoretical Perspective from Xiaohongshu
Bibliographic record
Abstract
Between 2023 and 2024, Xiaohongshu became one of the top five social media platforms in China. Social media use has both positive and negative effects on appearance satisfaction. This study explores how Xiaohongshus platform featureshomepage visualization, gender-based content recommendation, and public engagement (likes and comments)impact young Chinese womens appearance satisfaction through a multi-theoretical lens integrating use and gratification, gender schema, and objectification theories. A theoretical framework is applied to analyze each platform features psychological mechanisms, drawing on existing empirical evidence from comparable social media studies. The study synthesizes qualitative insights to hypothesize causal relationships between platform variables and appearance satisfaction. Findings suggest that Xiaohongshus features may reinforce appearance anxiety through prolonged image exposure, gender-stereotypical content, and objectifying feedback mechanisms. The study highlights implications for platform design and policy interventions, recommending features like optional like-count visibility to mitigate negative effects. Future empirical research is proposed to validate these relationships.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".