‘Everyone’s a bit buzzed, why not share that’: exploring alcohol-related user-generated content among young people in Victoria, Australia
Bibliographic record
Abstract
Social media platforms are increasingly saturated with alcohol-related user-generated content (UGC), which can shape young people's attitudes and behaviours towards drinking. While all young people are potentially influenced by this content, certain groups, such as Aboriginal young people; lesbian, gay, bisexual, transgender, queer, or other sexual and gender minorities (LGBTQ+) young people; and those living in regional areas, experience disproportionate alcohol-related harms and may have unique experiences with alcohol-related UGC. However, research examining these diverse perspectives remains limited. This qualitative study explored perspectives of Aboriginal, LGBTQ+, and regional young people (aged 16-20) regarding alcohol-related social media practices through semi-structured interviews (n = 24). Reflexive thematic analysis was applied, with four overarching themes constructed from the data: (i) participants described alcohol posting as performative practice tied to sociability, identity, and peer influence; (ii) social media posts and digital amplification were seen to embed binge drinking culture within youth identity; (iii) Aboriginal, LGBTQ+, and regional participants reported distinct responses to alcohol UGC, with experiences shaped by stereotyping, safety concerns, and permissive environments; (iv) influencer-generated content was viewed as highly pervasive and many participants expressed scepticism at its authenticity. Policy action is needed to protect young people from the harms associated with online alcohol promotion and must recognize the intersectional experiences of young people from Aboriginal, LGBTQ+ and regional communities.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".