| Full text | |
| Author(s): |
Rodrigues, Michele Bittencourt
;
Ferreira, Victoria Pedrazzoli
;
Claro, Rafael Moreira
;
Martins, Ana Paula Bortoletto
;
Avila, Sandra
;
Horta, Paula Martins
Total Authors: 6
|
| Document type: | Journal article |
| Source: | PUBLIC HEALTH NUTRITION; v. 26, n. 12, p. 11-pg., 2023-11-10. |
| 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 center dot 5 % on the test database, which represents a reduction of 99 center dot 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. (AU) | |
| FAPESP's process: | 13/08293-7 - CCES - Center for Computational Engineering and Sciences |
| Grantee: | Munir Salomao Skaf |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 20/09838-0 - BI0S - Brazilian Institute of Data Science |
| Grantee: | João Marcos Travassos Romano |
| Support Opportunities: | Research Grants - Research Centers in Engineering Program |