Social Media Friendship Jealousy
Bibliographic record
Abstract
A new measure to assess friendship jealousy in the context of social media was developed. This one-factor, seven-item measure was psychometrically sound, showing evidence of validity and reliability in three samples of North American adults (Study 1, n = 491; Study 2, n = 494; Study 3, n = 415) and one-, two-, and three-year stability (Study 3). Women reported more social media friendship jealousy than men (Studies 2 and 3) and younger women had the highest levels of social media friendship jealousy (compared with younger men and older men and women; Study 2). Social media friendship jealousy was associated with lower friendship quality (Study 1) and higher social media use and trait jealousy (Study 2). The relation between social media friendship jealousy and internalizing symptoms indicated positive within time associations and longitudinal bidirectional relations (Study 3). Specifically, social media friendship jealousy predicted increases in internalizing problems, and internalizing problems predicted greater social media friendship jealousy accounting for gender and trait levels of social media friendship jealousy and internalizing problems. Anxious and depressed adults may be predisposed to monitor threats to their friendships via social media and experience negative consequences because of this behavior. Although social media interactions can be associated with positive well-being and social connectedness, our results highlight that they can also undermine friendships and mental health due to jealousy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".