The extent of energy drink marketing on Canadian social media
Bibliographic record
Abstract
Abstract Background Caffeinated energy drink (CED) consumption among children and adolescents is a growing global public health concern due to its potential to produce adverse effects. CED marketing viewed by children and adolescents contributes to this problem as it increases consumption and favourable attitudes towards these high-caffeine and high-sugar products. This study aimed to describe the social media marketing of CEDs by estimating the frequency of user-generated and company-generated CED marketing and analyzing the marketing techniques used by Canadian CED brands on social media. Methods CED products and brands were identified using the list of CEDs that received a Temporary Marketing Authorization from Health Canada in June 2021. The data on the frequency, reach and engagement of CED-related posts created by users and Canadian CED brands on Facebook, Instagram, Twitter, Reddit, Tumblr, and YouTube were licensed from Brandwatch for 2020–2021. A content analysis was conducted to assess the marketing techniques used in Canadian CED company-generated posts using a coding manual. Results A total of 72 Canadian CED products were identified. Overall, there were 222,119 user-level mentions of CED products in total and the mentions reached an estimated total of 351,707,901 users across platforms. The most popular product accounted for 64.8% of the total user-level mentions. Canadian social media company-owned accounts were found for 27 CED brands. Two CED brands posted the most frequently on Twitter and accounted for the greatest reach, together making up 73.9% of the total company-level posts and reaching 62.5% of the total users in 2020. On Instagram/Facebook, the most popular brand accounted for 23.5% of the company-level posts and 81.3% of the reach between July and September 2021. The most popular marketing techniques used by Canadian CED brands were the use of viral marketing strategies (82.3% of Twitter posts and 92.5% of Instagram/Facebook posts) and the presence of teen themes (73.2% of Twitter posts and 39.4% of Instagram/Facebook posts). Conclusion CED companies are extensively promoting their products across social media platforms using viral marketing strategies and themes that may appeal to adolescents. These findings may inform CED regulatory decision-making. Continued monitoring is warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".