Picturing food: the visual style of teen-targeted food marketing
Bibliographic record
Abstract
Purpose Despite the pervasiveness of teen-targeted food advertising on social media, little is known about the persuasive elements (or power) found within those ads. This research study aims to engage with the concept of “visual style” to explore the range of visual techniques used in Instagram food marketing to teenagers. Design/methodology/approach A participatory study was conducted with 57 teenagers, who used a specially designed mobile app to capture images of the teen-targeted food marketing they encountered for seven days. A visual thematic analysis was used to assess and classify the advertisements that participants captured from Instagram and specifically tagged with “visual style”. Findings A total of 142 food advertisements from Instagram were tagged with visual style, and classified into five main styles: Bold Focus, Bespoke, Absurd, Everyday and Sensory. Research limitations/implications This study contributes to an improved understanding about how the visual is used as a marketing technique to capture teenagers’ attention, contributing to the persuasive power of marketing messages. Originality/value Food marketing is a significant part of the young consumer’s marketplace, and this study provides new insight into the sophisticated nature of such marketing – revealing the visual styles used to capture the attention of its brand-aware audience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".