The Timing, Nature and Extent of Social Media Marketing by Unhealthy Food and Drinks Brands During the COVID-19 Pandemic in New Zealand
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».