Exposure to and engagement with gambling marketing in social media: Reported impacts on moderate-risk and problem gamblers.
Bibliographic record
Abstract
Digital advertising for gambling and specifically marketing via social media have increased in recent years, and the impact on vulnerable consumers, including moderate-risk and problem gamblers, is unknown. Social media promotions often fall outside of advertising restrictions and codes of conduct and may have an inequitable effect on susceptible gamblers. This study aimed to investigate recall of exposure to, and reported impact on gamblers of, gambling promotions and marketing content on social media, with a focus on vulnerable users currently experiencing gambling problems. Gamblers who use social media (N = 964) completed an online survey assessing their exposure to and engagement with gambling operators on social media, their problem gambling severity, and the impact of social media promotions on their gambling. Gamblers at moderate risk and problem gamblers were significantly more likely to report having been exposed to social media gambling promotions and indicated actively engaging with gambling operators via these platforms. They were more likely to self-report that they had increased gambling as a result of these promotions, and over one third reported that the promotions had increased their problems. This research suggests that gamblers at moderate risk or those experiencing gambling problems are more likely to be impacted by social media promotions, and these may play a role in exacerbating disordered gambling. Future research should verify these self-reported results with behavioral data. However, the potential influence of advertisements via these new platforms should be considered by clinicians and policymakers, given their potential role in the formation of this behavioral addiction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".