#ad on Instagram: Investigating the Promotion of Food and Beverage Products
Bibliographic record
Abstract
The recent rise in popularity of social networking sites has led to an associated increase in user-generated photographic content relating to various aspects of users’ personal lives. The sharing of food photography has become a popular means of social interaction between friends and strangers online, and has prompted companies in the food and beverage industry to shift their marketing objectives from the traditional top-down strategies to a more modern peer-to-peer approach. The current study investigated the promotion of food and beverage products on Instagram tagged with #ad. Specifically, the current study evaluated aspects of food and beverage images (N = 100) which garnered the most popularity (i.e., likes) among viewers, information about the author (e.g., credentials), as well as cues to like or comment on each image and the audience reaction to images. In evaluating the popularity of food and beverage images, a likes-to-follower ratio was calculated by dividing the number of likes on each image by the number of followers the author of the image had. Findings of this study indicated that images containing beverages, mainly consisting of protein or weight-loss drinks, were more popular compared to advertised food products (p = 0.026). In addition, the majority of authors were not considered credible sources of nutrition information (n = 94), and many did not list credentials (n = 89), indicating that advertised food and beverage products may not fully align with evidence-based guidelines that one would receive from a Registered Dietitian or other healthcare professional. The majority of comments on images were positive (M = 15.0, SD = 24.6), suggesting a low message resistance to food and beverage products advertised on Instagram. Results of this research have implications for public health initiatives targeted towards marketing food and beverage products on social networking sites.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".