Evidence map›Paper›PMID 41167120›Full record

ArticleSocial science & medicine (1982)2026

A decade of discourse: Exploring sentiments and trends around immigration on social media from 2014 to 2024.

Thu T Nguyen, Penchala Sai Priya Mullaputi, Xiaohe Yue, Heran Mane, Adwaith Santhosh, Elizabeth Dennard, Amrutha S Alibilli, Katrina Makres, Rozalina G McCoy, Quynh C Nguyen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Social science & medicine (1982), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

10 authors.

Thu T NguyenDepartment of Epidemiology and Biostatistics, University of Maryland, College Park, College Park, MD, United States. Electronic address: ttxn@umd.edu.
Penchala Sai Priya MullaputiDepartment of Epidemiology and Biostatistics, University of Maryland, College Park, College Park, MD, United States.
Xiaohe YueDepartment of Epidemiology and Biostatistics, University of Maryland, College Park, College Park, MD, United States.
Heran ManeDepartment of Epidemiology and Biostatistics, University of Maryland, College Park, College Park, MD, United States.
Adwaith SanthoshDepartment of Data Science, College of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, College Park, MD, United States.
Elizabeth DennardDepartment of Epidemiology and Biostatistics, University of Maryland, College Park, College Park, MD, United States.
Amrutha S AlibilliDepartment of Epidemiology and Biostatistics, University of Maryland, College Park, College Park, MD, United States.
Katrina MakresDepartment of Epidemiology and Biostatistics, University of Maryland, College Park, College Park, MD, United States.
Rozalina G McCoyDivision of Gerontology, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, United States; University of Maryland Institute for Health Computing, North Bethesda, MD, United States; Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States.
Quynh C NguyenDepartment of Epidemiology and Biostatistics, University of Maryland, College Park, College Park, MD, United States.

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
NIMHD NIH HHS R01 MD015716
6 · The paper itself

Abstract

introductionSocial media discussions contribute to the evolving public perception of refugees and immigrants. However, prior research often relied on a single platform and short-term analyses, offering a fragmented view of a highly dynamic phenomenon.

objectiveExamine trends in public narratives surrounding refugees and immigrants, including the evolution of sentiment and user engagement on Twitter, Facebook, and Bluesky.

methodsWe analyzed 6.3 million U.S.-based English-language posts from Twitter (2014-2023), Facebook (2014-2024), and Bluesky (2023-2024), using platform APIs. Posts containing one or more of 129 immigration-related keywords were grouped into 76 categories. Sentiment was classified using a supervised Support Vector Machine model, and engagement was aggregated at the keyword level. Twitter geodata enabled state-level sentiment mapping.

resultsPeaks in volume and negativity aligned with major events, including the 2014 Syrian refugee crisis, the 2017 travel ban, and the 2018 family separation policy. From 2014 to 2019, negative sentiment increased on both Twitter and Facebook, then became more neutral in subsequent years. Bluesky began with predominantly neutral discourse in 2023 but grew more negative after its public launch. Refugee-related discourse was consistently less negative than immigrant-related discourse across all platforms, while enforcement-related and exclusionary rhetoric keywords emerged as the most negatively evaluated. Twitter geodata revealed widespread negativity across states, although refugee discourse remained more moderate or neutral than immigrant discourse overall.

conclusionMigration discourse is shaped by political events, emotional framing, and platform-specific dynamics, underscoring the need for cross-platform analyses to understand evolving digital narratives.

Indexed as

Emigrants and ImmigrantsEmigration and ImmigrationRefugeesSocial MediaHumansUnited StatesImmigrantsRefugeesSentiment analysisSocial mediaUser engagement

Identifiers

PMID41167120
PMCPMC13062689

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.