Likelihood of Posting Alcohol-Related Content on Social Networking Sites – Measurement Development and Initial Validation
Bibliographic record
Abstract
Background: The vast majority of adolescents and young adults are active on social networking sites (SNSs). SNSs are influential, risk-conducive environments for alcohol use among adolescents and young adults. Specifically, posting or sharing alcohol-related content (ARC) is associated with higher levels of alcohol use. However, it is unknown if sharing different types of ARC associates differentially with alcohol use and consequences. Objective: The goal of the current project was to develop a measure of the likelihood of posting key types of ARC posted by adolescents and young adults and to examine their associations with SNS use patterns and actual alcohol-related behavior. Method: Participants were 15–20 years of age (n = 306; 46.7% male; 56.6% Caucasian/White; 27.0% Asian) who completed a battery of self-report measures. Results: Results from an exploratory factor analysis revealed four types of ARC: (1) self and friend consumption, (2) memes and viral photos, (3) status updates: others’ drinking and consequences, and (4) pictures: others’ drinking and consequences. Conclusions: Participants’ likelihood of posting self and Friend Consumption was significantly associated with heightened Snapchat use, typical drinks per week, peak drinking, and negative drinking consequences. Whereas youth appear to share more readily alcohol-related viral posts and memes, it seems that the sharing of ARC that is specifically related to the participants’ own use or friends’ use is salient concerning alcohol use and problems. Therefore, interventions might consider sending targeted prevention messages to individuals who share certain types of ARC which are more associated with problematic alcohol behaviors.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".