Lane JM, Zhang X, Alcala CS, Midya V, Nagdeo K, Li R, Wright RO. Tweeting environmental pollution: Analyzing twitter language to uncover its correlation with county-level obesity rates in the United States.
Prev Med 2024;
186:108081. [PMID:
39038770 DOI:
10.1016/j.ypmed.2024.108081]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 07/17/2024] [Accepted: 07/18/2024] [Indexed: 07/24/2024]
Abstract
BACKGROUND
Environmental pollution has been linked to obesogenic tendencies. Using environmental-related posts from Twitter (now known as X) from U.S. counties, we aim to uncover the association between Twitter linguistic data and U.S. county-level obesity rates.
METHODS
Analyzing nearly 300 thousand tweets from January 2020 to December 2020 across 207 U.S. counties, using an innovative Differential Language Analysis technique and drawing county-level obesity data from the 2020 Food Environment Atlas to identify distinct linguistic features in Twitter relating to environmental-related posts correlated with socioeconomic status (SES) index indicators, obesity rates, and obesity rates controlled for SES index indicators. We also employed predictive modeling to estimate Twitter language's predictive capacity for obesity rates.
RESULTS
Results revealed a negative correlation between environmental-related tweets and obesity rates, both before and after adjusting for SES. Contrarily, non-environmental-related tweets showed a positive association with higher county-level obesity rates, indicating that individuals living in counties with lower obesity rates tend to tweet environmental-related language more frequently than those living in counties with higher obesity rates. The findings suggest that linguistic patterns and expressions employed in discussing environmental-related themes on Twitter can offer unique insights into the prevailing cross-sectional patterns of obesity rates.
CONCLUSIONS
Although Twitter users are a subset of the general population, incorporating environmental-related tweets and county-level obesity rates and using a novel language analysis technique make this study unique. Our results indicated that Twitter users engaging in more active dialog about environmental concerns might exhibit healthier lifestyle practices, contributing to reduced obesity rates.
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