Empowering health geography research with location-based social media data: innovative food word expansion and energy density prediction via word embedding and machine learning.

ABSTRACT:

Background

The exponential growth of location-based social media (LBSM) data has ushered in novel prospects for investigating the urban food environment in health geography research. However, previous studies have primarily relied on word dictionaries with a limited number of food words and employed common-sense categorizations to determine the healthiness of those words. To enhance the analysis of the urban food environment using LBSM data, it is crucial to develop a more comprehensive list of food-related words. Within the context, this study delves into the exploration of expanding food-related words along with their associated energy densities.

Methods

This study addresses the aforementioned research gap by introducing a novel methodology for expanding the food-related w

SUBMITTER: Wang J 

PROVIDER: S-EPMC10505329 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature