Analyzing Instagram Food and Nutrition Posts Through a Food Literacy Lens: Content Analysis of Instagram Posts
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".