COVID-Washing in US Food and Beverage Marketing on Twitter: Content Analysis
Notice bibliographique
Résumé
BACKGROUND: Food companies have increased digital and social media ad expenditures during the COVID-19 pandemic, capitalizing on the coinciding increase in social media use during the pandemic. The extent of pandemic-related social media advertising and marketing tactics have been previously reported. No studies, however, have evaluated how food and beverage companies used COVID-washing on social media posts in the United States or analyzed the nutritional content of advertised food and beverage products. This study was designed to address these gaps by evaluating how food and beverage companies capitalize on the COVID-19 pandemic to promote unhealthy foods and sugary beverages. OBJECTIVE: We aimed to document the types and frequencies of COVID-19-related themes in US food and beverage companies' Twitter posts during the first wave of the pandemic in the United States, and assess the nutritional quality of food and beverage products featured in these tweets. METHODS: Research assistants visited the Twitter accounts of the most-marketed food and beverage brands, and screen-captured all tweets posted between March 1 and May 31, 2020. Researchers documented the date of the tweet; the number of likes, views, comments, and "retweets"; and the type of food and beverage products. We coded tweets for the following 10 COVID-19 themes: (1) social distancing, staying home, or working remotely; (2) contactless delivery or pick-up; (3) handwashing or sanitizing; (4) masks; (5) safety or protection; (6) staying connected with others; (7) staying active; (8) frontline or essential workers; (9) monetary relief, donations, or unemployment; and (10) pandemic, unprecedented, or difficult times. Researchers calculated the nutrient profile index scores for featured foods and sorted beverages into categories based on sugar content. RESULTS: Our final sample included 874 COVID-19-themed tweets from 52 food and beverage brands. Social distancing themes appeared most frequently (n=367, 42%), followed by pandemic, unprecedented, or difficult times (n=246, 28.2%), and contactless delivery (n=237, 27.1%). The majority of tweets (n=682, 78%) promoted foods and beverages. Among those tweets featuring foods and beverages, 89.6% (n=611) promoted unhealthy products, whereas 17.2% (n=117) promoted healthy products. CONCLUSIONS: Our findings point to a concerning marketing tactic in which major food and beverage companies promote unhealthy foods and sugary beverages during the COVID-19 pandemic. Given that nutrition-related diseases such as obesity and diabetes are risk factors for COVID-19 morbidity and mortality, food and beverage companies should reduce the promotion of unhealthy products to help decrease the prevalence of health conditions that place people at higher risk for severe illness and death due to COVID-19.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,005 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».