Department of Applied Computer Science, University of Winnipeg, 515 Portage Avenue, Winnipeg, MBR3B 2E9, Canada.
Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Alberta, Canada.
Public Health Nutr. 2023 Jun;26(6):1125-1142. doi: 10.1017/S1368980023000642. Epub 2023 Apr 3.
Children are frequently exposed to unhealthy food marketing on digital media. This marketing contains features that often appeal to children, such as cartoons or bold colours. Additional factors can also shape whether marketing appeals to children. In this study, in order to assess the most important predictors of child appeal in digital food marketing, we used machine learning to examine how marketing techniques and children's socio-demographic characteristics, weight, height, BMI, frequency of screen use and dietary intake influence whether marketing instances appeal to children.
We conducted a pilot study with thirty-nine children. Children were divided into thirteen groups, in which they evaluated whether food marketing instances appealed to them. Children's agreement was measured using Fleiss' kappa and the S score. Text, labels, objects and logos extracted from the ads were combined with children's variables to build four machine-learning models to identify the most important predictors of child appeal.
Households in Calgary, Alberta, Canada.
39 children aged 6-12 years.
Agreement between children was low. The models indicated that the most important predictors of child appeal were the text and logos embedded in the food marketing instances. Other important predictors included children's consumption of vegetables and soda, sex and weekly hours of television.
Text and logos embedded in the food marketing instances were the most important predictors of child appeal. The low agreement among children shows that the extent to which different marketing strategies appeal to children varies.
儿童经常会接触到数字媒体上的不健康食品营销。这些营销内容通常包含吸引儿童的元素，如卡通或鲜艳的颜色。此外，还有其他因素也会影响营销是否能吸引儿童。在这项研究中，为了评估数字食品营销中吸引儿童的最重要预测因素，我们使用机器学习来研究营销技巧以及儿童的社会人口统计学特征、体重、身高、BMI、屏幕使用频率和饮食摄入如何影响营销实例是否吸引儿童。
我们进行了一项有 39 名儿童参与的试点研究。儿童被分为 13 组，每组评估食品营销实例是否吸引他们。使用 Fleiss' kappa 和 S 分数来衡量儿童的一致性。从广告中提取的文本、标签、对象和徽标与儿童的变量相结合，构建了四个机器学习模型，以确定吸引儿童的最重要预测因素。
加拿大阿尔伯塔省卡尔加里的家庭。
39 名 6-12 岁的儿童。
儿童之间的一致性较低。模型表明，吸引儿童的最重要预测因素是食品营销实例中嵌入的文本和徽标。其他重要的预测因素包括儿童对蔬菜和苏打水的消费、性别和每周看电视的时间。
食品营销实例中嵌入的文本和徽标是吸引儿童的最重要预测因素。儿童之间的一致性较低表明，不同营销策略吸引儿童的程度不同。
