Use of persuasive strategies in food advertising on television and on social media in Brazil
Bibliographic record
Abstract
We analyzed the use of persuasive advertising strategies by 18 food brands on TV and Facebook, Instagram, and YouTube in Brazil in April 2018. Advertising strategies were investigated from three groups: power of advertising strategies (n = 10) (e.g., use of licensed character, celebrities, awards, etc), use of the prize offering (n = 9) (e.g., pay 2 take 3 or more, gifts or collectable, limited edition, etc), and use of brand benefit claims (n = 8) (e.g., messages that exalt sensory-based characteristics such as flavor, taste, aroma and recommend how to use/consume the product, etc). Almost 90% of the brands were ultra-processed foods producers and they carried 52 ads on TV and 194 posts on social media platforms. A higher frequency of the strategy 'cartoon/company owned character' was found on TV ads (19.2%; p < 0.0001) in comparison to social media platforms (0% on the three platforms) while the presence of 'famous sportsperson/team' prevailed on YouTube (41.4%) in comparison to TV (19.2%), Facebook (10.9%) and Instagram (9.1%), p < 0.0001. On YouTube ads, the claims 'sensory-based characteristics' (86.2%), 'suggested use' (51.7%), and 'emotive claims' (31.0%) were more commonly seen in comparison to the other media, while the claims about 'new brand developments' (23.1%), 'price' (9.6%) and 'suggesting to children and the whole family to use the advertised product' (21.1%) prevailed on TV. Ultra-processed food brands are the main food companies that advertise on Brazilian TV and social media and the message transmitted by these brands varies in each media according to the advertising strategies that are used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.010 |
| 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 | 0.000 |
| 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".