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Development of an Artificial Intelligence System to Monitor Digital Marketing of Unhealthy Food to Children: Research Protocol

OBJECTIVES: Unhealthy food marketing to children adversely affects their diet quality and health. The negative impacts of this marketing may be amplified on digital media, which allows industry to use artificial intelligence (AI) to market unhealthy food to children in covert ways. Health Canada is...

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Autores principales: Olstad, Dana Lee, Raman, Munib, Valderrama, Camilo, Abad, Zahra Shakeri Hossein, Cheema, Abdullah Bashir, Ng, Steven, Memon, Ashar, Lee, Joon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9193991/
http://dx.doi.org/10.1093/cdn/nzac072.023
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author Olstad, Dana Lee
Raman, Munib
Valderrama, Camilo
Abad, Zahra Shakeri Hossein
Cheema, Abdullah Bashir
Ng, Steven
Memon, Ashar
Lee, Joon
author_facet Olstad, Dana Lee
Raman, Munib
Valderrama, Camilo
Abad, Zahra Shakeri Hossein
Cheema, Abdullah Bashir
Ng, Steven
Memon, Ashar
Lee, Joon
author_sort Olstad, Dana Lee
collection PubMed
description OBJECTIVES: Unhealthy food marketing to children adversely affects their diet quality and health. The negative impacts of this marketing may be amplified on digital media, which allows industry to use artificial intelligence (AI) to market unhealthy food to children in covert ways. Health Canada is developing regulations to prohibit digital marketing of unhealthy food that appeals to children <13 years. However, reliance on adults to manually assess food marketing to children on digital media has limited understanding of key targets for policy and capacity to monitor policy adherence. To address these gaps, we are developing an AI system to monitor marketing of unhealthy food to children on digital media, including websites, YouTube, social media and mobile gaming apps. METHODS: Our web and mobile scrapers continuously collect marketing instances that may be viewed by individuals in Canada on websites and social media applications popular with children. This has allowed us to accumulate a database of > 615,000 marketing instances. The AI system extracts features from each marketing instance to determine whether foods are present, and if so, whether they are unhealthy according to Health Canada's standards (based on the presence of added saturated fat, added sodium and/or free sugars). Next, the AI system uses a supervised machine learning model to assess whether child appealing marketing techniques are present. In the final step, the system integrates all of the data collected to determine whether a given marketing instance features unhealthy foods and appeals to children. The system can be applied to monitor the extent and nature of digital food marketing to children internationally. It can also be retrained to monitor adherence to country-specific policy. RESULTS: This is a protocol paper so there are no results. CONCLUSIONS: The AI system provides a scalable, objective and reproducible manner to identify digital marketing of unhealthy food that appeals to children across the digital marketing landscape. The system can assist researchers and policy makers to study children's exposure to digital marketing of unhealthy food and its impacts, and to monitor adherence to policy that restricts this marketing. FUNDING SOURCES: Canadian Institutes of Health Research.
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spelling pubmed-91939912022-06-14 Development of an Artificial Intelligence System to Monitor Digital Marketing of Unhealthy Food to Children: Research Protocol Olstad, Dana Lee Raman, Munib Valderrama, Camilo Abad, Zahra Shakeri Hossein Cheema, Abdullah Bashir Ng, Steven Memon, Ashar Lee, Joon Curr Dev Nutr Protocols OBJECTIVES: Unhealthy food marketing to children adversely affects their diet quality and health. The negative impacts of this marketing may be amplified on digital media, which allows industry to use artificial intelligence (AI) to market unhealthy food to children in covert ways. Health Canada is developing regulations to prohibit digital marketing of unhealthy food that appeals to children <13 years. However, reliance on adults to manually assess food marketing to children on digital media has limited understanding of key targets for policy and capacity to monitor policy adherence. To address these gaps, we are developing an AI system to monitor marketing of unhealthy food to children on digital media, including websites, YouTube, social media and mobile gaming apps. METHODS: Our web and mobile scrapers continuously collect marketing instances that may be viewed by individuals in Canada on websites and social media applications popular with children. This has allowed us to accumulate a database of > 615,000 marketing instances. The AI system extracts features from each marketing instance to determine whether foods are present, and if so, whether they are unhealthy according to Health Canada's standards (based on the presence of added saturated fat, added sodium and/or free sugars). Next, the AI system uses a supervised machine learning model to assess whether child appealing marketing techniques are present. In the final step, the system integrates all of the data collected to determine whether a given marketing instance features unhealthy foods and appeals to children. The system can be applied to monitor the extent and nature of digital food marketing to children internationally. It can also be retrained to monitor adherence to country-specific policy. RESULTS: This is a protocol paper so there are no results. CONCLUSIONS: The AI system provides a scalable, objective and reproducible manner to identify digital marketing of unhealthy food that appeals to children across the digital marketing landscape. The system can assist researchers and policy makers to study children's exposure to digital marketing of unhealthy food and its impacts, and to monitor adherence to policy that restricts this marketing. FUNDING SOURCES: Canadian Institutes of Health Research. Oxford University Press 2022-06-14 /pmc/articles/PMC9193991/ http://dx.doi.org/10.1093/cdn/nzac072.023 Text en © The Author 2022. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Protocols
Olstad, Dana Lee
Raman, Munib
Valderrama, Camilo
Abad, Zahra Shakeri Hossein
Cheema, Abdullah Bashir
Ng, Steven
Memon, Ashar
Lee, Joon
Development of an Artificial Intelligence System to Monitor Digital Marketing of Unhealthy Food to Children: Research Protocol
title Development of an Artificial Intelligence System to Monitor Digital Marketing of Unhealthy Food to Children: Research Protocol
title_full Development of an Artificial Intelligence System to Monitor Digital Marketing of Unhealthy Food to Children: Research Protocol
title_fullStr Development of an Artificial Intelligence System to Monitor Digital Marketing of Unhealthy Food to Children: Research Protocol
title_full_unstemmed Development of an Artificial Intelligence System to Monitor Digital Marketing of Unhealthy Food to Children: Research Protocol
title_short Development of an Artificial Intelligence System to Monitor Digital Marketing of Unhealthy Food to Children: Research Protocol
title_sort development of an artificial intelligence system to monitor digital marketing of unhealthy food to children: research protocol
topic Protocols
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9193991/
http://dx.doi.org/10.1093/cdn/nzac072.023
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