Influencing emotion: Social anxiety and comparisons on Instagram.
Bibliographic record
Abstract
Individuals with social anxiety are sensitive to social hierarchies and tend to compare themselves unfavorably with others, perceiving themselves as inferior or lower in social rank. The current study explores patterns of change in these negative perceptions, and their associated emotional outcomes, in an online social context. Undergraduate students (N = 291) browsed the profiles of eight Instagram influencers and completed a measure of social comparison after viewing each profile, yielding multiple ratings of their own perceived social rank. Participants completed measures of affect and state self-esteem before and after the profile browsing task. Higher social anxiety predicted lower, and greater declines in, social rank self-perceptions during browsing. Higher social anxiety also predicted greater decreases in positive affect, increases in negative affect, and decreases in state self-esteem from the beginning to the end of the browsing task. Low baseline rank perceptions contributed to change in all three emotional variables. Decreases in rank perceptions contributed further to decreases in positive affect and appearance-related self-esteem. This study elaborates on cognitive-evolutionary theory, suggesting that the inferiority self-perceptions of socially anxious individuals translate to online social contexts, may be strengthened with increased exposure to such contexts, and may have a detrimental emotional impact. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".