The new age of tobacco marketing: Imagery on social media
Bibliographic record
Abstract
Background While many areas of tobacco marketing have been restricted, the rise of digital and social media has presented an opportunity for new marketing avenues. This study looked at celebrity social media culture and its contribution to tobacco marketing. Objectives: - Assess the prevalence of such marketing. - Assess who is being targeted, which audience segments. - Determine what types of tobacco products are being marketed. - Analyze themes or frames used to make these products appealing. Methods Qualitative and quantitative methods were used to assess the celebrity social media setting. The top 10 global celebrity personalities were included for assessment. Only Instagram accounts were included as sources for data collection. Instagram posts were reviewed for imagery of tobacco products, content, and audience targets. From this data a thematic analysis was conducted to assess frames and narratives used within marketing tactics. Review of social media posts was limited to a 3 year time frame starting October 1, 2014 and ending September 30, 2017. Results Tobacco related marketing remains present on social media within the celebrity culture. Many of the top 10 followed celebrities had portrayals of tobacco embedded within their profile and/or posts. Young females are overtly targeted and traditional themes such as fashion, glamour, fun, sex appeal and free-will continue to be used as marketing frames. Conclusions These findings demonstrate the need for intervention to monitor and restrict tobacco related marketing on social media. While it's difficult to determine the source of tobacco marketing social media (industry placed or without paid placement) it warrants a discussion about influence of the celebrity and digital cultures on health behaviours. In the absence of regulation, celebrities should be considerate and responsible for the harmful imagery shared over social media.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".