ArticleHeliyon2020
Online negative sentiment towards Mexicans and Hispanics and impact on mental well-being: A time-series analysis of social media data during the 2016 United States presidential election.
Article in Heliyon, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Area-level racial prejudice and health: A systematic review.Health psychology : official journal of the Division of Health Psychology, American Psychological Association · 2022Pooled it
- Race and Anti-Racist Online Coping as Moderators of College Adjustment Associated with Exposure to Social Media Racism among Asian, Black, and Hispanic/Latine Students.Journal of youth and adolescence · 2025Article
- From Tweets to Streets: Observational Study on the Association Between Twitter Sentiment and Anti-Asian Hate Crimes in New York City from 2019 to 2022.Journal of medical Internet research · 2024Observational
- Structural Stigma, Racism, and Sexism Studies on Substance Use and Mental Health: A Review of Measures and Designs.Alcohol research : current reviews · 2024Review
- The Water Surrounding the Iceberg: Cultural Racism and Health Inequities.The Milbank quarterly · 2023Article
- Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health.Annual review of public health · 2022Review
- Parental perceived immigration threat and children's mental health, self-regulation and executive functioning in pre-Kindergarten.The American journal of orthopsychiatry · 2022Article
- Association of "#covid19" Versus "#chinesevirus" With Anti-Asian Sentiments on Twitter: March 9-23, 2020.American journal of public health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe purpose was to use Twitter to conduct online surveillance of negative sentiment towards Mexicans and Hispanics during the 2016 United States presidential election, and to examine its relationship with mental well-being in this targeted group at the population level.
methodsTweets containing the terms Mexican(s) and Hispanic(s) were collected within a 20-week period of the 2016 United States presidential election (November 9th 2016). Sentiment analysis was used to capture percent negative tweets. A time series lag regression model was used to examine the association between percent count of negative tweets mentioning Mexicans and Hispanics and percent count of worry among Hispanic Gallup poll respondents.
resultsOf 2,809,641 tweets containing terms Mexican(s) and Hispanic(s), 687,291 tweets were negative. Among 8,314 Hispanic Gallup respondents, a mean of 33.5% responded to be worried on a daily basis. A significant lead time of 1 week was observed, showing that negative tweets mentioning Mexicans and Hispanics appeared to forecast daily worry among Hispanics by 1 week.
conclusionSurveillance of online negative sentiment towards racially vulnerable population groups can be captured using social media. This has potential to identify early warning signals for symptoms of mental well-being among targeted groups at the population level.
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Registered trials
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.