The Timing, Nature and Extent of Social Media Marketing by Unhealthy Food and Drinks Brands During the COVID-19 Pandemic in New Zealand
Bibliographic record
Abstract
Background: Concerns have been raised that health and societal causes surrounding the COVID-19 pandemic were misappropriated by companies to promote their unhealthy products to vulnerable populations during a time of increased stress and hardship (i.e., COVID-washing). Social media is a common medium for unhealthy foods and beverage marketing due to lack of regulation and low levels of monitoring. Purpose: This study aimed to investigate the timing, nature and extent of COVID-washing on public social media accounts by New Zealand's major food and drink brands in the initial stage of the pandemic after the first case was detected in New Zealand and when stay-at-home lockdown restrictions (Level 4 and 3 Alert levels) were in place. Methods: A content analysis of social media posts from February to May 2020 by the twenty largest confectionery, snacks, non-alcoholic beverages, and quick-service restaurant (fast-food) brands was undertaken. COVID-19 related posts were identified and classified to investigate the timing, themes and engagement with social media marketing campaigns, flagging those that may breach New Zealand's Advertising Standards. Results: 14 of 20 unhealthy food and drink brands referenced COVID-19 in posts during the 4-month period, peaking during nationwide lockdown restrictions. Over a quarter of all posts by the 14 brands (n = 372, 27.2%) were COVID-19 themed. Fast-food brands were most likely to use COVID-19 themed posts (n = 251/550 posts, 46%). Fast-food brands also had the highest number of posts overall during the pandemic and the highest engagement. The most commonly-used theme, present in 36% of all social media posts referring to COVID-19, was to draw on feelings of community support during this challenging time. Suggesting brand-related isolation activities was also common (23%), and the message that “consumption helps with coping” (22%). Six posts were found to potentially breach one of New Zealand's advertising standards codes by promoting excessive consumption or targeting children. Conclusion: COVID-washing was used by unhealthy food and drinks brands to increase brand loyalty and encourage consumption. The current Advertising Standards system is ineffective and must be replaced with a government-led approach to effectively regulate social media advertising to protect all New Zealanders, particularly in times of crisis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".