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A Novel Approach to characterize State Level food environment and Predict Obesity Rate Using Social Media Data
Chuqin Li;
Alex Jordan;
Jun Song;
yaorong ge;
Albert Park
ABSTRACT
The food environment is associated with resident’s obesity outcomes. Social media data from Yelp and MyFitnessPal (MFP) were used to learn about the relationship between food environments and obesity rates at the state level. We first compared the differences in food category availability between two states with lowest and highest obesity rates: Colorado and Mississippi. Using the popular dishes for food category from Yelp.com and the nutrition information for each popular dish from MFP, we characterized the local food environment by averaging calories for each category and the weighted score for each state. The Pearson correlation coefficient between state’s food environment and state obesity rate is statistically significant. Dimensions from the concept of access were adopted to build computational models to predict state-level obesity rate. We achieved a Pearson correlation of 0.791 across US states and the District of Columbia.
Citation
Please cite as:
Li C, Jordan A, Song J, ge y, Park A
A Novel Approach to Characterize State-level Food Environment and Predict Obesity Rate Using Social Media Data: Correlational Study