Content Hungry: How the Nutrition of Food Media Influences Social Media Engagement
Bibliographic record
Abstract
What motivates people to consume and engage with food media on social networks? We adopt an evolutionary lens to suggest that the valence of people’s affective state varies by the implied caloric density of food media, which has a direct impact on social media engagement. First, we analyze a catalog of Buzzfeed’s Tasty videos based on nutritional content derived from the dish’s ingredients and find that visualizing caloric density (i.e., calories per serving) positively influences likes, comments, and shares on Facebook. We then replicate this phenomenon in an experiment, providing preliminary evidence for the role of affect as an explanatory mechanism. We conclude by isolating the role of affect with a classic misattribution task, which attenuates the elevated engagement resulting from exposure to calorie‐dense food media. These findings contribute to the dialogue on the antecedents of social media engagement and offer implications for content developers, advertisers, consumer health advocates, and policymakers.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".