Evidence map›Paper›PMID 31633756›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2020

Uncovering the relationship between food-related discussion on Twitter and neighborhood characteristics.

V G Vinod Vydiswaran, Daniel M Romero, Xinyan Zhao, Deahan Yu, Iris Gomez-Lopez, Jin Xiu Lu, Bradley E Iott, Ana Baylin, Erica C Jansen, Philippa Clarke and 3 more

Open access · bronzeAbstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
3.3field-weighted citation impact, top 9% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

9 citing papers in PubMed, 30 citations in OpenAlex.

  1. Article
  2. Observational
  3. Article
  4. Article
  5. Exploring the Linkages of Digital Food Communication and Analog Food Behavior: A Scoping Review.International journal of environmental research and public health · 2022
    Article
  6. Article
  7. Article
  8. Article
  9. Review
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

13 authors at 3 institutions in 1 country.

V G Vinod VydiswaranDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.
Daniel M RomeroSchool of Information, University of Michigan, Ann Arbor, Michigan, USA.
Xinyan ZhaoSchool of Information, University of Michigan, Ann Arbor, Michigan, USA.
Deahan YuSchool of Information, University of Michigan, Ann Arbor, Michigan, USA.
Iris Gomez-LopezInstitute for Social Research, University of Michigan, Ann Arbor, Michigan, USA.
Jin Xiu LuSchool of Information, University of Michigan, Ann Arbor, Michigan, USA.
Bradley E IottSchool of Information, University of Michigan, Ann Arbor, Michigan, USA.
Ana BaylinDepartment of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Erica C JansenDepartment of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Philippa ClarkeInstitute for Social Research, University of Michigan, Ann Arbor, Michigan, USA.
Veronica J BerrocalDepartment of Statistics, Donald Bren School of Information and Computer Science, University of California, Irvine, California, USA.
Robert GoodspeedUrban and Regional Planning Program, Taubman College of Architecture and Urban Planning, University of Michigan, Ann Arbor, Michigan, USA.
Tiffany C VeinotSchool of Information, University of Michigan, Ann Arbor, Michigan, USA.
University of Michigan · USMichigan Department of Health and Human Services · USUniversity of California, Irvine · US

Funding

Research BaseP30DK092926 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MARY ELLEN MICHELE HEISLER, ADESUWA B OLOMU · 2011 to 2026
$10.0M
NIDDK NIH HHS P30 DK092926
6 · The paper itself

Abstract

objectiveInitiatives to reduce neighborhood-based health disparities require access to meaningful, timely, and local information regarding health behavior and its determinants. We examined the validity of Twitter as a source of information for neighborhood-level analysis of dietary choices and attitudes. MATERIALS AND

methodsWe analyzed the "healthiness" quotient and sentiment in food-related tweets at the census tract level, and associated them with neighborhood characteristics and health outcomes. We analyzed keywords driving the differences in food healthiness between the most and least-affluent tracts, and qualitatively analyzed contents of a random sample of tweets.

resultsSignificant, albeit weak, correlations existed between healthiness and sentiment in food-related tweets and tract-level measures of affluence, disadvantage, race, age, U.S. density, and mortality from conditions associated with obesity. Analyses of keywords driving the differences in food healthiness revealed foods high in saturated fat (eg, pizza, bacon, fries) were mentioned more frequently in less-affluent tracts. Food-related discussion referred to activities (eating, drinking, cooking), locations where food was consumed, and positive (affection, cravings, enjoyment) and negative attitudes (dislike, personal struggles, complaints). DISCUSSION: Tweet-based healthiness scores largely correlated with offline phenomena in the expected directions. Social media offer less resource-intensive data collection methods than traditional surveys do. Twitter may assist in informing local health programs that focus on drivers of food consumption and could inform interventions focused on attitudes and the food environment.

conclusionsTwitter provided weak but significant signals concerning food-related behavior and attitudes at the neighborhood level, suggesting its potential usefulness for informing local health disparity reduction efforts.

Indexed as

DietFoodResidence CharacteristicsSocial MediaHealth Status DisparitiesHumansPopulation DensityRegression AnalysisSocioeconomic FactorsUnited Stateshealth equityhealthy dietnatural language processingpopulation healthsocial media

Identifiers

PMID31633756
PMCPMC7025333
OpenAlexW2981575630

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.