Evidence map›Paper›PMID 39038770›Full record

ArticlePreventive medicine2024

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

Jamil M Lane, Xupin Zhang, Cecilia S Alcala, Vishal Midya, Kiran Nagdeo, Rui Li, Robert O Wright

Abstract read
In one paragraph

Article in Preventive medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jamil M LaneDepartment of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: jamil.lane@mssm.edu.
Xupin ZhangSchool of Economics and Management, East China Normal University, Shanghai, China.
Cecilia S AlcalaDepartment of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Vishal MidyaDepartment of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Kiran NagdeoDepartment of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Rui LiSchool of Economics and Management, East China Normal University, Shanghai, China.
Robert O WrightDepartment of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Institute for Climate Change, Environmental Health, and Exposomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Funding

The Mount Sinai Transdisciplinary Center on Early Environmental ExposuresP30ES023515 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Rosalind J Wright · 2014 to 2026
$21.3M
Stress, Lead, Iron Deficiency and NeurodevelopmentR01ES013744 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Robert O Wright · 2007 to 2026
$13.4M
Research Training Program in Pediatric ExposomicsT32HD049311 · NICHD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KECIA Nicole CARROLL, Robert O Wright · 2007 to 2026
$7.4M
The Programming Research in Obesity, GRowth, Environment and Social Stress (PROGRESS) CohortU24ES028522 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI HORTON, MEGAN K, ROSA, MARIA JOSE · 2024 to 2025
$719k
Phthalate Mixtures and the Interconnection of Child Neurobehavior and ObesityK99ES036277 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI LANE, JAMIL MARLO · 2024 to 2024
$106k
A Community Partnered Approach to Assessing the Impact of Prenatal Pesticide Exposure on Child Respiratory Outcomes in Mexico CityK99ES035894 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI ALCALA, CECILIA SARA · 2024 to 2024
$106k
NICHD NIH HHS T32 HD049311NIEHS NIH HHS K99 ES035894NIEHS NIH HHS K99 ES036277NIEHS NIH HHS L40 ES034609NIEHS NIH HHS P30 ES023515NIEHS NIH HHS R01 ES013744NIEHS NIH HHS U24 ES028522
6 · The paper itself

Abstract

backgroundEnvironmental 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.

methodsAnalyzing 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.

resultsResults 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.

conclusionsAlthough 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.

Indexed as

Environmental PollutionObesitySocial MediaHumansLanguageUnited StatesEnvironmental pollutionMachine learningObesity ratesSocial mediaTwitter language

Identifiers

PMID39038770
PMCPMC11785377

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.