The Nature and Extent of Online Marketing by Big Food and Big Alcohol During the COVID-19 Pandemic in Australia: Content Analysis Study
Bibliographic record
Abstract
BACKGROUND: Emerging evidence demonstrates that obesity is associated with a higher risk of COVID-19 morbidity and mortality. Excessive alcohol consumption and "comfort eating" as coping mechanisms during times of high stress have been shown to further exacerbate mental and physical ill-health. Global examples suggest that unhealthy food and alcohol brands and companies are using the COVID-19 pandemic to further market their products. However, there has been no systematic, in-depth analysis of how "Big Food" and "Big Alcohol" are capitalizing on the COVID-19 pandemic to market their products and brands. OBJECTIVE: We aimed to quantify the extent and nature of online marketing by alcohol and unhealthy food and beverage companies during the COVID-19 pandemic in Australia. METHODS: We conducted a content analysis of all COVID-19-related social media posts made by leading alcohol and unhealthy food and beverage brands (n=42) and their parent companies (n=12) over a 4-month period (February to May 2020) during the COVID-19 pandemic in Australia. RESULTS: Nearly 80% of included brands and all parent companies posted content related to COVID-19 during the 4-month period. Quick service restaurants (QSRs), food and alcohol delivery companies, alcohol brands, and bottle shops were the most active in posting COVID-19-related content. The most common themes for COVID-19-related marketing were isolation activities and community support. Promotion of hygiene and home delivery was also common, particularly for QSRs and alcohol and food delivery companies. Parent companies were more likely to post about corporate social responsibility (CSR) initiatives, such as donations of money and products, and to offer health advice. CONCLUSIONS: This is the first study to show that Big Food and Big Alcohol are incessantly marketing their products and brands on social media platforms using themes related to COVID-19, such as isolation activities and community support. Parent companies are frequently posting about CSR initiatives, such as donations of money and products, thereby creating a fertile environment to loosen current regulation or resist further industry regulation. "COVID-washing" by large alcohol brands, food and beverage brands, and their parent companies is both common and concerning. The need for comprehensive regulations to restrict unhealthy food and alcohol marketing, as recommended by the World Health Organization, is particularly acute in the COVID-19 context and is urgently required to "build back better" in a post-COVID-19 world.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".