Evidence map›Paper›PMID 37115602›Full record

ArticleJournal of medical Internet research2023

Examining Twitter-Derived Negative Racial Sentiment as Indicators of Cultural Racism: Observational Associations With Preterm Birth and Low Birth Weight Among a Multiracial Sample of Mothers, 2011-2021.

Thu T Nguyen, Junaid S Merchant, Shaniece Criss, Katrina Makres, Krishik N Gowda, Heran Mane, Xiaohe Yue, Yulin Hswen, M Maria Glymour, Quynh C Nguyen and 1 more

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.3field-weighted citation impact, top 11% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Observational
  2. Article
  3. Observational
  4. Article
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

11 authors at 4 institutions in 1 country.

Thu T Nguyen *Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.ORCID 0000-0003-1185-045X
Junaid S Merchant *Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.ORCID 0000-0002-4315-6211
Shaniece Criss *Department of Health Sciences, Furman University, Greenville, SC, United States.ORCID 0000-0002-2470-5377
Katrina Makres *Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.ORCID 0000-0003-0132-5512
Krishik N GowdaDepartment of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.ORCID 0000-0002-5553-9818
Heran Mane *Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.ORCID 0000-0001-8859-3282
Xiaohe Yue *Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.ORCID 0000-0001-5922-1949
Yulin HswenDepartment of Epidemiology and Biostatistics, Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States.ORCID 0000-0003-3203-1322
M Maria GlymourDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States.ORCID 0000-0001-9644-3081
Quynh C NguyenDepartment of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.ORCID 0000-0003-4745-6681
Amani M AllenDivisions of Community Health Sciences and Epidemiology, University of California, Berkeley, Berkeley, CA, United States.ORCID 0000-0002-5882-1026
University of Maryland, College Park · USUniversity of California, San Francisco · USFurman University · USUniversity of California, Berkeley · US

Funding

Risk and strength: determining the impact of area-level sentiment and protective factors on birth outcomesR01MD015716 · NIMHD · UNIV OF MARYLAND, COLLEGE PARK · PI NGUYEN, THU · 2021 to 2025
$3.6M
Rosie the Chatbot: Leveraging Automated and Personalized Health Information Communication to Reduce Disparities in Maternal and Child HealthR01MD016037 · NIMHD · UNIV OF MARYLAND, COLLEGE PARK · PI NGUYEN, THU, NORELL, ELIZABETH MARIE · 2021 to 2025
$3.3M
Neighborhood Looking Glass: 360 Degree Automated Characterization of the Built Environment for Neighborhood Effects ResearchR01LM012849 · NLM · UNIV OF MARYLAND, COLLEGE PARK · PI NGUYEN, QUYNH · 2018 to 2021
$1.3M
Place-level discrimination and birth outcomesR00MD012615 · NIMHD · UNIV OF MARYLAND, COLLEGE PARK · PI NGUYEN, THU · 2020 to 2022
$720k
NIMHD NIH HHS R00 MD012615NIMHD NIH HHS R01 MD015716NIMHD NIH HHS R01 MD016037NLM NIH HHS R01 LM012849
6 · The paper itself

Abstract

backgroundLarge racial and ethnic disparities in adverse birth outcomes persist. Increasing evidence points to the potential role of racism in creating and perpetuating these disparities. Valid measures of area-level racial attitudes and bias remain elusive, but capture an important and underexplored form of racism that may help explain these disparities. Cultural values and attitudes expressed through social media reflect and shape public norms and subsequent behaviors. Few studies have quantified attitudes toward different racial groups using social media with the aim of examining associations with birth outcomes.

objectiveWe used Twitter data to measure state-level racial sentiments and investigate associations with preterm birth (PTB) and low birth weight (LBW) in a multiracial or ethnic sample of mothers in the United States.

methodsA random 1% sample of publicly available tweets from January 1, 2011, to December 31, 2021, was collected using Twitter's Academic Application Programming Interface (N=56,400,097). Analyses were on English-language tweets from the United States that used one or more race-related keywords. We assessed the sentiment of each tweet using support vector machine, a supervised machine learning model. We used 5-fold cross-validation to assess model performance and achieved high accuracy for negative sentiment classification (91%) and a high F1 score (84%). For each year, the state-level racial sentiment was merged with birth data during that year (~3 million births per year). We estimated incidence ratios for LBW and PTB using log binomial regression models, among all mothers, Black mothers, racially minoritized mothers (Asian, Black, or Latina mothers), and White mothers. Models were controlled for individual-level maternal characteristics and state-level demographics.

resultsMothers living in states in the highest tertile of negative racial sentiment for tweets referencing racial and ethnic minoritized groups had an 8% higher (95% CI 3%-13%) incidence of LBW and 5% higher (95% CI 0%-11%) incidence of PTB compared to mothers living in the lowest tertile. Negative racial sentiment referencing racially minoritized groups was associated with adverse birth outcomes in the total population, among minoritized mothers, and White mothers. Black mothers living in states in the highest tertile of negative Black sentiment had 6% (95% CI 1%-11%) and 7% (95% CI 2%-13%) higher incidence of LBW and PTB, respectively, compared to mothers living in the lowest tertile. Negative Latinx sentiment was associated with a 6% (95% CI 1%-11%) and 3% (95% CI 0%-6%) higher incidence of LBW and PTB among Latina mothers, respectively.

conclusionsTwitter-derived negative state-level racial sentiment toward racially minoritized groups was associated with a higher risk of adverse birth outcomes among the total population and racially minoritized groups. Policies and supports establishing an inclusive environment accepting of all races and cultures may decrease the overall risk of adverse birth outcomes and reduce racial birth outcome disparities.

Indexed as

Pregnancy ComplicationsPremature BirthRacismSocial MediaAttitudeFemaleHumansInfant, Low Birth WeightInfant, NewbornMothersRacial GroupsUnited Statesbirth outcomeshealth disparitiesmachine learning, racial sentimentsocial media

Identifiers

PMID37115602
PMCPMC10182466
OpenAlexW4367311505

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.