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

Public (e)Health, Digital Epidemiology and Public Health Informatics (847) Machine Learning (1698) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1774) Instruments and Questionnaires for Nutrition and Food Intake (182) Infodemiology and Infoveillance (1445) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1085) Food Sciences and Nutrition Engineering (55) Innovations and Technology for Healthy Eating Education (443)

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 (2) Tweetations (3) Metrics

Journals

  1. Jordan A, Park A. Understanding the Long Haulers of COVID-19: Mixed Methods Analysis of YouTube Content. JMIR AI 2024;3:e54501 View

Conference Proceedings

  1. Reddy K, Beenarani B. INTERNATIONAL CONFERENCE ON APPLICATION OF ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SOURCES AND ENVIRONMENTAL SUSTAINABILITY. Improved accuracy of food recognition and calorie measurement system using novel linear regression and extreme gradient boosting View