Analyzing Instagram Food and Nutrition Posts Through a Food Literacy Lens: Content Analysis of Instagram Posts
Notice bibliographique
Résumé
BACKGROUND: Dietary behaviors are directly linked to health and well-being. Food literacy education may improve poor dietary behaviors and thus, health and well-being. Social media is a popular source of food literacy education through content delivered by influencers and experts alike. Characterizing food and nutrition content on social media using a food literacy framework can identify gaps in public food literacy knowledge and opportunities for improving food literacy education. OBJECTIVE: The primary objective of this study was to systematically characterize and categorize publicly available food- and nutrition-related Instagram content according to food literacy concepts. METHODS: We conducted a mixed methods study using content analysis. We collected Instagram posts that used hashtags related to the term "healthy eating" via CrowdTangle. We completed our content analysis using Netlytic to categorize posts according to our framework of food literacy and topics of interest. Then, we completed a descriptive qualitative content analysis of a sub-sample of posts from each category. RESULTS: Our analysis included 100,000 Instagram posts. We categorized the Instagram posts using 19 categories related to food literacy and attitudes to healthy eating. The most frequent categories were (1) information about foods to consume (38,500/100,000, 38.5%), (2) cooking and preparing food (36,007/100,000, 36%), and (3) planning and managing food intake (33,262/100,000, 33.3%). Protein-rich foods, fiber, vegetables, juicing and smoothie diets, and spices were commonly promoted as foods to consume, while selecting organic and fresh foods was encouraged more frequently than canned or frozen foods. Processed and prepared foods were discouraged. Baking was frequently portrayed as a cooking method, as well as quick and easy recipes, and cooking with friends and family. Planning food intake was frequently discussed in relation to weight loss and holidays. Cultural foods were portrayed as healthy foods and with healthier variations, and in the context of holidays and religious observances. Low-cost and affordable foods were portrayed with minimal time requirements, minimal ingredients, and depicted as family-appropriate. CONCLUSIONS: Instagram content frequently portrayed healthy eating as part of a healthy lifestyle and impacting physical health, activity and energy levels, and mood. However, prescriptive information regarding foods to consume was still pervasive. Encouragement to cook together and share recipes together indicates the social aspect of eating and cooking as important to users and may be an important aspect of food literacy guidance and programs in the future. Our descriptive analysis of Instagram content demonstrates several opportunities for supporting and improving food literacy education on social media.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».