Tweeting environmental pollution: Analyzing twitter language to uncover its correlation with county-level obesity rates in the United States

IF 4.3 2区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Jamil M. Lane , Xupin Zhang , Cecilia S. Alcala , Vishal Midya , Kiran Nagdeo , Rui Li , Robert O. Wright
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引用次数: 0

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.

推特环境污染:分析 twitter 语言,揭示其与美国县级肥胖率的相关性。
背景:环境污染与肥胖倾向有关。我们利用美国各县推特(现在称为 X)上与环境相关的帖子,旨在揭示推特语言数据与美国县级肥胖率之间的关联:分析 2020 年 1 月至 2020 年 12 月期间美国 207 个县的近 30 万条推文,使用创新的差异语言分析技术,并从 2020 年食品环境地图集中提取县级肥胖数据,以确定推特中与环境相关帖子相关的独特语言特征,这些语言特征与社会经济地位(SES)指数指标、肥胖率以及控制 SES 指数指标的肥胖率相关。我们还采用了预测模型来估计推特语言对肥胖率的预测能力:结果显示,在调整社会经济地位指数之前和之后,环境相关推文与肥胖率之间均呈负相关。相反,与环境无关的推文与较高的县级肥胖率呈正相关,这表明生活在肥胖率较低的县的人往往比生活在肥胖率较高的县的人更频繁地在推特上发布与环境有关的语言。研究结果表明,在推特上讨论与环境相关的主题时所使用的语言模式和表达方式可以为了解肥胖率的普遍横截面模式提供独特的见解:尽管推特用户是普通人群的一个子集,但将与环境相关的推文和县级肥胖率纳入其中,并使用新颖的语言分析技术,使本研究独具特色。我们的研究结果表明,参与有关环境问题的更积极对话的推特用户可能会表现出更健康的生活方式,从而有助于降低肥胖率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Preventive medicine
Preventive medicine 医学-公共卫生、环境卫生与职业卫生
CiteScore
7.70
自引率
3.90%
发文量
0
审稿时长
42 days
期刊介绍: Founded in 1972 by Ernst Wynder, Preventive Medicine is an international scholarly journal that provides prompt publication of original articles on the science and practice of disease prevention, health promotion, and public health policymaking. Preventive Medicine aims to reward innovation. It will favor insightful observational studies, thoughtful explorations of health data, unsuspected new angles for existing hypotheses, robust randomized controlled trials, and impartial systematic reviews. Preventive Medicine''s ultimate goal is to publish research that will have an impact on the work of practitioners of disease prevention and health promotion, as well as of related disciplines.
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