ArticlePublic health nutrition2023
Revolutionising food advertising monitoring: a machine learning-based method for automated classification of food videos.
Article in Public health nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Seeing Is Ordering: The Influence of Mukbang Video Types on Consumers' Immediate Ordering Intention.Foods (Basel, Switzerland) · 2026Article
- Evaluating AI models for food and alcohol advertisement classification against human benchmarks.Scientific reports · 2026Article
- Artificial intelligence in functional food innovation: Bioactive enhancement and formulation optimization: A quasi-systematic review.Food chemistry: X · 2026Review
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Authors and funding
6 authors.
Funding
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Abstract
objectiveFood advertising is an important determinant of unhealthy eating. However, analysing a large number of advertisements (ads) to distinguish between food and non-food content is a challenging task. This study aims to develop a machine learning-based method to automatically identify and classify food and non-food ad videos.
designMethodological study to develop an algorithm model that prioritises both accuracy and efficiency in monitoring and classifying advertising videos.
settingFrom a collection of Brazilian television (TV) ads data, we created a database and split it into three sub-databases (i.e. training, validation and test) by extracting frames from ads. Subsequently, the training database was classified using the EfficientNet neural network. The best models and data-balancing strategies were investigated using the validation database. Finally, the test database was used to apply the best model and strategy, and results were verified with field experts.
participantsThe study used 2124 recorded Brazilian TV programming hours from 2018 to 2020. It included 703 food ads and over 20 000 non-food ads, following the protocol developed by the INFORMAS network for monitoring food marketing on TV.
resultsThe results showed that the EfficientNet neural network associated with the balanced batches strategy achieved an overall accuracy of 90·5 % on the test database, which represents a reduction of 99·9 % of the time spent on identifying and classifying ads.
conclusionsThe method studied represents a promising approach for differentiating food and non-food-related video within monitoring food marketing, which has significant practical implications for researchers, public health policymakers, and regulatory bodies.
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