Public Health Nutrition (Dec 2023)

Revolutionising food advertising monitoring: a machine learning-based method for automated classification of food videos

  • Michele Bittencourt Rodrigues,
  • Victória Pedrazzoli Ferreira,
  • Rafael Moreira Claro,
  • Ana Paula Bortoletto Martins,
  • Sandra Avila,
  • Paula Martins Horta

DOI
https://doi.org/10.1017/S1368980023002446
Journal volume & issue
Vol. 26
pp. 2717 – 2727

Abstract

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Abstract Objective: Food 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. Design: Methodological study to develop an algorithm model that prioritises both accuracy and efficiency in monitoring and classifying advertising videos. Setting: From 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. Participants: The 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. Results: The 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. Conclusions: The 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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