A novel approach to characterize state-level food environment and predict obesity rate using social media data : correlational study

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Ge, Y., Li, C., & Jordan, A. (2022). A novel approach to characterize state-level food environment and predict obesity rate using social media data : correlational study. Journal Of Medical Internet Research. https://doi.org/doi:10.2196/39340
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Description

Faculty, Community obesity outcomes can reflect the food environment to which the community belongs. Recent studies have suggested that the local food environment can be measured by the degree of food accessibility, and survey data are normally used to calculate food accessibility. However, compared with survey data, social media data are organic, continuously updated, and cheaper to collect.

Details
Title

A novel approach to characterize state-level food environment and predict obesity rate using social media data : correlational study

Author(s)

Ge, Yaorong
Li, Chuqin
Jordan, Alexis

UNC Charlotte Affiliate Type

Faculty

Extent

1 online resource

Date Created

2022

Genre

articles

Resource Type

text

Abstract

Community obesity outcomes can reflect the food environment to which the community belongs. Recent studies have suggested that the local food environment can be measured by the degree of food accessibility, and survey data are normally used to calculate food accessibility. However, compared with survey data, social media data are organic, continuously updated, and cheaper to collect.

Subjects--Topics

Obesity
Machine learning

Is Part Of

Journal of medical Internet research

Language

English

Published Version (Please cite this version)

doi:10.2196/39340

Publisher's Version

https://doi.org/10.2196/39340

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This item may be protected by copyright and other related rights. Atkins Library provides access to this item for educational and research purposes only; other uses require the permission of the copyright holder.