Boosting engagement with healthy food on social media
Bibliographic record
Abstract
Purpose Recent research has demonstrated that people are more likely to engage with fatty food content online. One way health advocates might facilitate engagement with healthier, calorie-light foods is to alter how people process food media. This research paper aims to investigate the moderating role of viewer mindset on consumer responses to digital food media. Design/methodology/approach Two experiments were conducted by manipulating the caloric density of food media content and/or one’s mindset before viewing. Findings Results show that the relationship between nutrition and engagement is moderated by consumer mindset, where activating a more calculative mindset before exposure can elevate social media engagement for calorie-light food media content. Research limitations/implications These findings contribute to the domain of obesogenic digital environments and the role of nutrition in consuming food media. By examining how mindsets interact with affective evaluations, this work demonstrates that a default mindset based on instinct can be shifted and thus alter subsequent behavioral intentions. Practical implications This work provides insight into what can boost the visibility and engagement of healthy food content on social media. Marketers can help promote healthier food media by cueing consumers to think more deliberately before exposure. Originality/value This research builds on recent work by demonstrating how to boost engagement with healthy foods on social media by cueing a more thoughtful mindset.
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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 | 0.044 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".