Evidence map›Paper›PMID 37531317›Full record

ArticlePloS one2023

Videos about older adults on TikTok.

Reuben Ng, Nicole Indran

Open access · goldAbstract read
In one paragraph

Article in PloS one, 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
3.4field-weighted citation impact, top 9% 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, 7 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Reuben NgLee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore.ORCID 0000-0002-1186-0570
Nicole IndranLee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore.ORCID 0000-0003-3095-2988
National University of Singapore · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Besides being one of the fastest growing platforms since entering the social media fray in 2016, TikTok is notably monopolized by teenagers, which makes it a veritable source of information not to be overlooked by gerontologists. Currently, most studies regarding age stereotypes on social media have examined content on Twitter and Facebook. Our study explores how older adults are portrayed on TikTok and the factors associated with these portrayals. We analyzed 673 videos with the hashtags #Boomer and/or #OkBoomer that received over 5.4 billion views and categorized them into nine topics. Five of these topics (e.g., 'Warmth/Coldness') were extracted from previous studies on age stereotypes. The remaining four topics were unique to our dataset (e.g., 'Wealth Gap'). The outcome variable was 'Negative Age Stereotypes' which was rated on a binary scale. One in two videos about older adults featured negative content. As hypothesized, videos containing negative age stereotypes were more likely to be about the 'Values and Beliefs of Older Adults' (7 times), 'Negative Encounters with Older Adults' (8 times) or 'Older Adults Antagonizing the Young' (13 times). Conversely, videos which portrayed older adults as 'Warm' were 43% less likely to contain negative stereotypes. As the phenomenon of an aging population fast unfolds, it is imperative that society relinquishes its tendency to stereotype individuals on the grounds of age. By examining the possible mechanisms driving negative stereotypes of older adults on TikTok, our study provides the basis upon which such stereotypes can be counteracted. In doing so, it paves the way both to improve the well-being of older persons and to foster intergenerational solidarity.

Indexed as

Social MediaAdolescentAgedAged, 80 and overAgingHumansStereotyping

Identifiers

PMID37531317
PMCPMC10395947
OpenAlexW4385489856

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.