Exploring the extent of digital food and beverage related content associated with a family-friendly event: a case study
Bibliographic record
Abstract
Background: Exposure to unhealthy food and beverage content is a contributing factor to the obesity epidemic. Youth are susceptible to unhealthy digital food marketing including content shared by their peers, which can be as influential as commercial marketing. Current Canadian regulations do not consider the threat digital food marketing poses to health. No research to date has examined the prevalence of food related posts on social media surrounding family-friendly events. The aim of this study was to explore the frequency of food related content (including food marketing) and the marketing techniques employed in social media posts related to a family-friendly event in Canada. Methods: In this case study, a content analysis of social media posts related to a family-friendly event on Facebook, Twitter, and Instagram was conducted between January to February 2019. Each post containing food related content was identified and categorized by source and food category using a coding manual. Marketing techniques found in each food related post were also assessed. Results: A total of 732 food and beverage related posts were assessed. These posts were most commonly promoted through Instagram (n = 561, 76.6%) with significantly more individual users (61.5%; p < 0.05) generating food and beverage related content (n = 198, 27%) than other post sources. The top most featured food category was fast food (n = 328, 44.8%) followed by dine-in restaurants (n = 126, 17.2%). The most frequently observed marketing techniques included predominantly featuring a child in the post (n = 124, 16.9%; p < 0.0001), followed by products intended for children (n = 118, 16.1%; p < 0.05), and the presence of family (n = 57, 7.8%; p < 0.0001). Conclusions: The present study highlights the proliferation of unhealthy food and beverage content by individuals at a family-friendly event as well as the presence of food marketing. Due to the unfettered advertising found in digital spaces, and that they are largely unregulated, it is important for future policies looking to combat childhood obesity to consider incorporating social media into their regulations to safeguard family-friendly events. General awareness on the implications of peer to peer sharing of unhealthy food and beverage posts should also be considered.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".