Exploring social contextual influences on healthy eating using big data analytics
Exploring social contextual influences on healthy eating using big data analytics
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DOI：
10.1109/bibm.2017.8217885
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
V. K. Yeruva;S. Junaid;Yugyung Lee
中科院分区：
文献类型：
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作者：
V. K. Yeruva;S. Junaid;Yugyung Lee
美国人口中超重的比例令人担忧：2/3的美国成年人超重，1/3的超重者肥胖。肥胖会增加患糖尿病和心血管疾病等疾病的风险。这种流行病可归因于廉价、高热量食物和缺乏体育活动的结合。在本文中，我们提出了一个大数据分析框架，名为Biobox，旨在探索社会背景对健康饮食的影响。为此，我们将食物推文和社交媒体图像分类为健康或不健康，以及食物情绪分为积极或消极，并进一步将其映射到肥胖患病率地图。分类结果将有助于揭示美国疾病控制预防中心（CDC）肥胖地区的社会食品趋势和情绪。Biocompatibility框架已经在Apache Spark和TensorFlow平台上实现。我们已经评估了Bioblog框架在食物推文分类和情感分析方面的准确性。实验结果表明，Bioblog框架是有效的分类和情感分析的食物鸣叫消息，也显示了其潜力，探索社会背景的影响，可能有助于健康饮食。
An alarming proportion of the US population is overweight: 2/3 of US adults are overweight, and 1/3 of those overweight are obese. Obesity increases the risk of illnesses such as diabetes and cardiovascular diseases. This epidemic can be attributed to the combination of cheap, high-calorie food and lack of physical activity. In this paper, we propose a Big Data Analytics framework, called BiDAF, that aims to explore social contextual influences on healthy eating. For this purpose, we classified food tweets and social media images into as either healthy or unhealthy as well as food sentiments into either positive or negative, and further mapped them to an obesity prevalence map. The classification outcomes would be useful to reveal the social food trends and sentiments of the Centers for Disease and Control Prevention (CDC) USA obesity regions. The BiDAF framework has been implemented on Apache Spark and TensorFlow platforms. We have evaluated the BiDAF framework in terms of the accuracy on the food tweet classification and sentiment analysis. The experimental results indicated that the BiDAF framework is effective in classification and sentiment analysis of food tweet messages and also showed its potential in exploring social contextual influences that may contribute to healthy eating.