https://doi.org/10.1007/s11069-020-04024-6

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Title

Dietary Pattern Recognition on Twitter:A Case Example of Before, During, and After Four Natural Disasters

Document Type

Article

Abstract

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.

Digital Object Identifier (DOI)

https://doi.org/10.1007/s11069-020-04024-6

APA Citation

Turner-McGrievy GM, Karami A, Monroe C, Brandt HM. (2020). Dietary pattern recognition on Twitter: A case example of before, during, and after four natural disasters. Natural Hazards, 103. https://doi.org/10.1007/s11069-020-04024-6

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