Evidence map›Paper›PMID 37698119›Full record

ArticleJMIR aging2023

Examining Public Awareness of Ageist Terms on Twitter: Content Analysis.

Emily Schramm, Christopher C Yang, Chia-Hsuan Chang, Kristine Mulhorn, Shushi Yoshinaga, Jina Huh-Yoo

Open access · goldAbstract read
In one paragraph

Article in JMIR aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
3.8field-weighted citation impact, top 7% 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

0 citing papers in PubMed, 8 citations in OpenAlex.

No citing paper in PubMed yet.

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

6 authors at 1 institution in 1 country.

Emily SchrammCollege of Medicine, Drexel University, Philadelphia, PA, United States.ORCID http://orcid.org/0009-0005-9500-1448
Christopher C YangDepartment of Information Science, College of Computing and Informatics, Drexel University, Philadelphia, PA, United States.ORCID http://orcid.org/0000-0001-5463-6926
Chia-Hsuan ChangDepartment of Information Science, College of Computing and Informatics, Drexel University, Philadelphia, PA, United States.ORCID http://orcid.org/0000-0001-9116-8244
Kristine MulhornHealth Administration Department, Drexel University, Philadelphia, PA, United States.ORCID http://orcid.org/0000-0001-9969-0010
Shushi YoshinagaWestphal College of Media Arts and Design, Drexel University, Philadelphia, PA, United States.ORCID http://orcid.org/0009-0003-2611-3265
Jina Huh-YooDepartment of Information Science, College of Computing and Informatics, Drexel University, Philadelphia, PA, United States.ORCID http://orcid.org/0000-0001-5811-9256
Drexel University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The World Health Organization, the Centers for Disease Control and Prevention, and the Gerontological Society of America have made efforts to raise awareness on ageist language and propose appropriate terms to denote the older adult population. The COVID-19 pandemic and older adults' vulnerability to the disease have perpetuated hostile ageist discourse on social media. This is an opportune time to understand the prevalence and use of ageist language and discuss the ways forward. Objective: This study aimed to understand the prevalence and situated use of ageist terms on Twitter. Methods: We collected 60.32 million tweets between March and July 2020 containing terms related to COVID-19. We then conducted a mixed methods study comprising a content analysis and a descriptive quantitative analysis. Results: A total of 58,930 tweets contained the ageist terms "old people" or "elderly." The more appropriate term "older adult" was found in 11,328 tweets. Twitter users used ageist terms (eg, "old people" and "elderly") to criticize ageist messages (17/60, 28%), showing a lack of understanding of appropriate terms to describe older adults. Highly hostile ageist content against older adults came from tweets that contained the derogatory terms "old people" (22/30, 73%) or "elderly" (13/30, 43%). Conclusions: The public discourse observed on Twitter shows a continued lack of understanding of appropriate terms to use when referring to older adults. Effort is needed to eliminate the perpetuation of ageist messages that challenge healthy aging. Our study highlights the need to inform the public about appropriate language use and ageism.

Indexed as

ageismagingCOVID-19diseaseelderlyinformaticslanguageolder adultspandemicpublicsocial mediatermterminologytweetsTwitter

Identifiers

PMID37698119
PMCPMC10507520
OpenAlexW4385394818

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.