Influencing emotion: Social anxiety and comparisons on Instagram.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it