This paper is in the following e-collection/theme issue:

Public (e)Health, Digital Epidemiology and Public Health Informatics (646) Machine Learning (1401) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1573) Instruments and Questionnaires for Nutrition and Food Intake (159) Infodemiology and Infoveillance (1278) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1015) Food Sciences and Nutrition Engineering (51) Innovations and Technology for Healthy Eating Education (400)

Published on in Vol 24, No 12 (2022): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39340, first published .
A Novel Approach to Characterize State-level Food Environment and Predict Obesity Rate Using Social Media Data: Correlational Study

A Novel Approach to Characterize State-level Food Environment and Predict Obesity Rate Using Social Media Data: Correlational Study

A Novel Approach to Characterize State-level Food Environment and Predict Obesity Rate Using Social Media Data: Correlational Study

Authors of this article:

Chuqin Li1 Author Orcid Image ;   Alexis Jordan1 Author Orcid Image ;   Jun Song2 Author Orcid Image ;   Yaorong Ge1 Author Orcid Image ;   Albert Park1 Author Orcid Image
Article Authors Cited by (1) Tweetations (3) Metrics

Chuqin Li   1 , PhD ;   Alexis Jordan   1 , BSc ;   Jun Song   2 , PhD ;   Yaorong Ge   1 , PhD ;   Albert Park   1 , PhD

1 Department of Software and Information Systems, College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC, United States

2 Department of Statistics, Korea University, Seoul, Republic of Korea

Corresponding Author:

  • Albert Park, PhD
  • Department of Software and Information Systems, College of Computing and Informatics
  • University of North Carolina at Charlotte
  • 9201 University Blvd
  • Charlotte, NC, 28223.
  • United States
  • Phone: 1 704 687 8668
  • Email: al.park@uncc.edu