Uploading Risk: Examining the Social Profile of Young Adults Most Susceptible to Engagement in Risky Social Media Challenges
Bibliographic record
Abstract
The aim of this study was to determine the social profile of individuals who are most at risk of engaging in risky social media challenges (RSMCs). Young adults (N = 331, 56.3 percent female) aged 18–25 years (Mage = 21.4) completed an online survey in which they indicated which RSMCs they had done (e.g., Cinnamon Challenge, Fire Challenge), and completed measures of social motives (i.e., need to belong, need for popularity, and fear of missing out [FoMO]) and perceived social status (i.e., popularity and peer belonging). Results demonstrated that almost half (48.3 percent) of participants had engaged in at least one RSMC. Furthermore, findings from a latent-class analysis revealed a three-class solution. Participants in Class 1 (stable social position, low social motives) had moderate-to-high probabilities for perceived popularity and peer belonging, but low probabilities for all three social motives. Participants in Class 2 (high perceived popularity and related concerns) had the highest probability for perceived popularity, need to be popular, and FoMO, and participants in Class 3 (high need to belong) had the highest probability for need to belong, but the lowest probabilities for need to be popular and perceived popularity. Although results differed somewhat by gender, overall, and in line with hypotheses, participants in Class 2 (high perceived popularity and related concerns) were most at risk for engagement in RSMCs. Thus, results suggest that engagement in RSMCs may be more about standing out and gaining online popularity and attention than about fitting in with peers. These findings contribute to a larger conversation about the provision of popularity markers on social media (likes, views) and their ability to shape young people's behavior.
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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 | 0.003 |
| 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 | 0.001 | 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".