气候变化领域集成服务门户(CCPortal): Dietary pattern recognition on Twitter: a case example of before, during, and after four natural disasters

CCPortal

DOI	10.1007/s11069-020-04024-6
	Dietary pattern recognition on Twitter: a case example of before, during, and after four natural disasters
	Turner-McGrievy G.; Karami A.; Monroe C.; Brandt H.M.
发表日期	2020
ISSN	0921030X
起始页码	1035
结束页码	1049
卷号	103 期号:1
英文摘要	Little is known about what foods/beverages (F&B) are common during natural disasters. The goal of this study was to track high-frequency F&B mentions during four hurricanes affecting the coast of South Carolina for quantifying dietary patterns in Twitter. A listing of common F&B (n = 173) was created from the top food sources of energy, fat, protein, and carbohydrate in the USA. A sampling of > 500,000 tweets containing hashtag names (e.g., #HurricaneFlorence) or actual names (e.g., “Hurricane Florence”) of the four hurricanes was collected using Crimson Hexagon. ANOVA was used to examine differences in number of mentions in each food group pre- (6 days before), during (48 h of the hurricane), and post-hurricane (6 days after). Descriptive statistics were used to examine the most frequently mentioned F&B (threshold defined as ≥ 4 mentions/day for each F&B item or 10% of the foods mentioned) and whether F&B were top sources of energy/macronutrients. More than 5000 mentions of F&B were collected in our sample. Grains were the most frequently mentioned food group pre-hurricane, and dairy was most frequently mentioned during the hurricanes. The top five most commonly mentioned F&B overall were milk (n = 517), pizza (n = 511), turkey (n = 425), oranges (n = 384), and waffles (n = 346). Foods mentioned were commonly energy and protein dense. Five foods (pizza, waffles, milk, rolls, and bread) were categorized as a top contributor across energy and all three macronutrients. Social media may be a unique way to detect dietary patterns and help inform public health social media campaigns to advise people about stocking up on healthy, non-perishable foods ahead of natural disasters. © 2020, Springer Nature B.V.
关键词	Diet Diet patterns Natural disaster Social media Twitter
英文关键词	diet; disaster management; food availability; food quality; food supply; hurricane event; information and communication technology; natural disaster; pattern recognition; quantitative analysis; social media; South Carolina; United States
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/205972
作者单位	Arnold School of Public Health, University of South Carolina, 915 Greene Street, Columbia, SC 29208, United States; School of Library and Information Science, University of South Carolina, 1501 Greene Street, Columbia, SC 29208, United States
推荐引用方式 GB/T 7714	Turner-McGrievy G.,Karami A.,Monroe C.,et al. Dietary pattern recognition on Twitter: a case example of before, during, and after four natural disasters[J],2020,103(1).
APA	Turner-McGrievy G.,Karami A.,Monroe C.,&Brandt H.M..(2020).Dietary pattern recognition on Twitter: a case example of before, during, and after four natural disasters.Natural Hazards,103(1).
MLA	Turner-McGrievy G.,et al."Dietary pattern recognition on Twitter: a case example of before, during, and after four natural disasters".Natural Hazards 103.1(2020).