The Impact of Short Video Content on Users' Self-Perception of Body Image and Appearance: An Empirical Study
Bibliographic record
Abstract
At present, short video platforms are becoming increasingly popular, which has gradually affected many audiences' cognition of life and self. This study investigates how short video platforms affect the public's attitudes towards their own appearance and body image, focusing on the perceptions and behaviors of a diverse sample of individuals who are primarily composed of individuals. The survey found that although beauty and fashion are not the top concerns for all users, users still focus on the person's appearance and body image when watching unrelated videos. This suggests that users may be unconsciously anxious about their appearance, which leads them to focus more on facial and physical features than on the core of the video content. Women show higher attention in the Fashion & Beauty, Culinary & Food, and Travel categories and compare themselves more often. But the rest of the users reported that they don't easily succumb to popular aesthetic standards, and that social media use moderately impacts mood. Analysis revealed gender disparities, notably with female users displaying heightened interest in fashion, beauty, and self-comparison behaviors. The study acknowledges limitations in sample size and demographic focus on Chinese-speaking populations, limiting broader applicability. Methodologically, it relied on self-reported data due to challenges in anonymously tracking user behavior across platforms, occasionally causing respondent confusion despite efforts to clarify survey wording. Future research should explore male users' experiences and how short video platforms affect self-awareness of facial and bodily features. This study advances understanding of digital media's impact on body image perception and calls for further investigation across diverse demographics.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".