Normalizing junk food: The frequency and reach of posts related to food and beverage brands on social media
Bibliographic record
Abstract
Food and beverage marketing on social media contributes to poor diet quality and health outcomes for youth, given their vulnerability to marketing's effects and frequent use of social media. This study benchmarked the reach and frequency of earned and paid media posts, an understudied social media marketing strategy, of food brands frequently targeting Canadian youth. The 40 food brands with the highest brand shares in Canada between 2015 and 2020 from frequently marketed food categories were determined using Euromonitor data. Digital media engagement data from 2020 were licensed from Brandwatch, a social intelligence platform, to analyze the frequency and reach of brand-related posts on Twitter, Reddit, Tumblr, and YouTube. The 40 food brands were mentioned on Twitter, Reddit, Tumblr, and YouTube a total of 16.85M times, reaching an estimated 42.24B users in 2020. The food categories with the most posts and reach were fast food restaurants (60.5% of posts, 58.1% of total reach) and sugar sweetened beverages (29.3% of posts, 37.9% of total reach). More men mentioned (2.77M posts) and were reached (6.88B users) by the food brands compared to women (2.47M posts, 5.51B users reached). The food and beverage brands (anonymized), with the most posts were fast food restaurant 2 (26.5% of the total posts), soft drink 2 (10.4% of the total posts), and fast food restaurant 6 (10.1% of the total posts). In terms of reach, the top brands were fast food restaurant 2 (33.1% of the total reach), soft drink 1 (18.1% of the total reach), and fast food restaurant 6 (12.2% of the total reach). There is a high number of posts on social media related to food and beverage brands that are popular among children and adolescents, primarily for unhealthy food categories and certain brands. The conversations online surrounding these brands contribute to the normalization of unhealthy food and beverage intake. Given the popularity of social media use amongst of children and adolescents, policies aiming to protect these vulnerable groups need to include the digital food environment.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".