Evidence map›Paper›PMID 35486433›Full record

ArticleJournal of medical Internet research2022

Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review.

Su Golder, Robin Stevens, Karen O'Connor, Richard James, Graciela Gonzalez-Hernandez

Abstract readScoping Review
In one paragraph

Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

5 authors.

Su Golder *Department of Health Sciences, University of York, York, United Kingdom.ORCID 0000-0002-8987-5211
Robin StevensSchool of Communication and Journalism, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-0481-9983
Karen O'ConnorDepartment of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.ORCID 0000-0001-7709-3813
Richard JamesSchool of Nursing Liaison and Clinical Outreach Coordinator, University of Pennsylvania, Philadelphia, PA, United States.ORCID 0000-0003-1672-0259
Graciela Gonzalez-HernandezDepartment of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.ORCID 0000-0002-6416-9556

Funding

UTILIZING TWITTER TO IDENTIFY AND RECRUIT YOUNG BLACK MEN AT HIGH RISK OF HIV INFECTIONR21DA049572 · NIDA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI STEVENS, ROBIN · 2019 to 2020
$450k
NIDA NIH HHS R21 DA049572
6 · The paper itself

Abstract

backgroundA growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population; however, the suitability of social media data in digital epidemiology is more nuanced. Identifying the demographics of social media users can help establish representativeness.

objectiveThis study aims to identify the different approaches or combination of approaches to extract race or ethnicity from social media and report on the challenges of using these methods.

methodsWe present a scoping review to identify methods used to extract the race or ethnicity of Twitter users from Twitter data sets. We searched 17 electronic databases from the date of inception to May 15, 2021, and carried out reference checking and hand searching to identify relevant studies. Sifting of each record was performed independently by at least two researchers, with any disagreement discussed. Studies were required to extract the race or ethnicity of Twitter users using either manual or computational methods or a combination of both.

resultsOf the 1249 records sifted, we identified 67 (5.36%) that met our inclusion criteria. Most studies (51/67, 76%) have focused on US-based users and English language tweets (52/67, 78%). A range of data was used, including Twitter profile metadata, such as names, pictures, information from bios (including self-declarations), or location or content of the tweets. A range of methodologies was used, including manual inference, linkage to census data, commercial software, language or dialect recognition, or machine learning or natural language processing. However, not all studies have evaluated these methods. Those that evaluated these methods found accuracy to vary from 45% to 93% with significantly lower accuracy in identifying categories of people of color. The inference of race or ethnicity raises important ethical questions, which can be exacerbated by the data and methods used. The comparative accuracies of the different methods are also largely unknown.

conclusionsThere is no standard accepted approach or current guidelines for extracting or inferring the race or ethnicity of Twitter users. Social media researchers must carefully interpret race or ethnicity and not overpromise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers and be guided by concerns of equity and social justice.

Indexed as

Social MediaData CollectionEthnicityHumansMachine LearningNatural Language Processingethnicityracesocial mediatwitter

Identifiers

PMID35486433
PMCPMC9107046

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.