Use of Social Media, Search Queries, and Demographic Data to Assess Obesity Prevalence in the United States.

ABSTRACT: Obesity is a global epidemic affecting millions. Implementation of interventions to curb obesity rates requires timely surveillance. In this study, we estimated sex-specific obesity prevalence using social media, search queries, demographics and built environment variables. We collected 3,817,125 and 1,382,284 geolocated tweets on food and exercise respectively, from Twitter's streaming API from April 2015 to March 2016. We also obtained searches related to physical activity and diet from Google Search Trends for the same time period. Next, we inferred the gender of Twitter users using machine learning methods and applied mixed-effects state-level linear regression models to estimate obesity prevalence. We observed differences in discussions of physical activity and foods, with males repor

SUBMITTER: Cesare N 

PROVIDER: S-EPMC7357895 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature