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RESEARCH ARTICLE
Videos about older adults on TikTok
Reuben NgID 1,2*, Nicole IndranID
1
1 Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore, 2 Lloyd’s
Register Foundation Institute for the Public Understanding of Risk, National University of Singapore,
Singapore, Singapore
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.
Introduction
In its initial coinage, the term ‘Baby Boomer’ referred to those born between the early 1940s to
the early 1960s, when the United States experienced a spike in the number of births after
World War II. Today, the generational label has taken on a new meaning. Described by some
as ‘Boomer Blaming’ [1], Baby Boomers are frequently pinpointed in public discourse as
responsible for a bevy of social and economic issues [2]. In 2019, the phrase ‘Ok, Boomer’
went viral on social media, reflecting widespread beliefs among younger people that Baby
Boomers are hindrances to societal progress [3]. By 2030, all Baby Boomers would be at least
65 years of age [4]. To bridge the generational divide and reap the benefits of intergenerational
interaction [5], it is first necessary to determine the factors linked to negative stereotypes of
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OPEN ACCESS
Citation: Ng R, Indran N (2023) Videos about older
adults on TikTok. PLoS ONE 18(8): e0285987.
https://doi.org/10.1371/journal.pone.0285987
Editor: Chang Sup Park, University of Albany, State
University of New York, UNITED STATES
Received: November 11, 2022
Accepted: May 5, 2023
Published: August 2, 2023
Copyright: © 2023 Ng, Indran. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All data were
collected from www.tiktok.com. Data collection
methodology is described in the paper for
reproducibility.
Funding: We gratefully acknowledge the support of
the Social Science Research Council SSHR
Fellowship (MOE2018-SSHR-004). The funders
had no role in study design, data collection and
analysis, decision to publish, or preparation of the
manuscript.
Competing interests: The authors have declared
that no competing interests exist.
older adults. Our study sheds light on this topic by analyzing videos with the hashtags
#OkBoomer and/or #Boomer on TikTok.
Significance of study and hypotheses
Conceptually, this study is significant in that it is among the first to explore stereotypes of
older adults on TikTok. Besides being one of the fastest growing platforms since entering the
social media fray in 2016 [6]—the application took merely five years to hit a billion monthly
active users [7]—TikTok is notably monopolized by teenagers [8], which makes it a veritable
source of information not to be overlooked by gerontologists. Furthermore, age stereotypes
are conventionally measured through surveys or questionnaires [9]. However, an analysis of
videos may present nuances not captured by regular survey-based methods. In addition, older
persons have recently begun embracing TikTok [10, 11]. As the usage of social media can affect
the well-being and quality of life of older persons [12–14], it is vital to understand how mem-
bers of this cohort are being portrayed by their younger contemporaries on TikTok. From a
practical perspective, this study provides insights into the mechanisms linked to negative ste-
reotypes of older persons, which will in turn lay the foundation for improving their well-being
as well as for cultivating intergenerational solidarity.
Existing literature on the way older persons are stereotyped on social media has looked pri-
marily at Facebook [15], Twitter [16–19] and Weibo [20], with only a handful mining content
on TikTok. A content analysis of descriptions of publicly accessible Facebook groups about
older people discovered that negative age stereotypes predominated [15]. Ageism was espe-
cially apparent on Twitter during the COVID-19 crisis [16–19, 21], though calls for intergener-
ational solidarity were also observed on the application [17, 18]. A thematic analysis of posts
uploaded on Weibo—a Chinese microblogging site with features that resemble those on Twit-
ter—revealed that during the pandemic, older people were constructed as warm and compe-
tent individuals who actively exercised their agency [20].
Research in gerontology that centers on TikTok revolves largely around the use of the appli-
cation among older adults [10, 11]. Only one study has examined how older persons are ste-
reotyped by younger people on the platform [22]. This study unpacked videos on TikTok that
featured younger people expressing a sense of hostility towards Baby Boomers [22]. Our study
differs from the aforementioned in two key aspects. First, unlike the previous study which
employed a qualitative approach, our study is quantitative in nature. This allows for a larger
number of videos to be analyzed, which will in turn facilitate a more comprehensive under-
standing of the portrayal of older adults on TikTok. Second, while the previous study focused
solely on videos where younger people evaluated Baby Boomers negatively, the current study
analyzes videos with and without negative age stereotypes. Hence, our study lends a broader
and more nuanced perspective on how older adults are viewed by younger people on TikTok.
Since Butler’s [23] pioneering work on ageism, age stereotypes have become a major subject
of gerontological interest. Although negative views dominate the image of older people [24–
27], it should be noted that stereotypes of them are not wholly negative. Instead, the broad cat-
egory of the older person is highly complex and differentiated into multiple subcategories [28,
29]. Positive stereotypes include being warm, generous and kind [30], while negative stereo-
types include being slow, irrelevant and incompetent [24]. Recent studies have shown that age
stereotypes in the United States have become more negative over the last two centuries
[31, 32]—an alarming trend given that the assimilation of negative age stereotypes into one’s
self-concept may adversely impact one’s health [33].
Various theories at the micro, meso and macro levels have been proffered to uncover the
etiology of ageism [34]. One of these is social identity theory. Social identity refers to the
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aspects of one’s self-concept that are derived from one’s membership in a particular social
group [35, 36]. At the core of social identity theory is the idea that individuals categorize them-
selves on the basis of perceived similarities and differences. In order to maintain a positive
social identity, people engage in intergroup evaluations that favor the ingroup and discrimi-
nate against the outgroup [35, 36]. Individuals may also express negative attitudes towards out-
group members when the value of an ingroup is attacked [37]. Evidence has shown that people
tend to denigrate younger generations [38]. Thus, we hypothesize that content about ‘Older
Adults Antagonizing the Young’ will be linked to negative stereotypes of the older population
(Hypothesis 1a).
Another perspective commonly used to account for ageism is that of intergroup threat the-
ory. The theory proposes that prejudice towards particular social groups is rooted in the per-
ception that these groups pose two kinds of threat: realistic and symbolic [39]. Realistic threats
refer to threats to the group’s power, resources and welfare, while symbolic threats are threats
to one’s worldview, belief system and values. Since Baby Boomers are frequently deemed to be
obstructing the more progressive ideologies of the younger generation [3], we hypothesize that
‘Values and Beliefs of Older Adults’ will be linked to negative stereotypes of this population
(Hypothesis 1b). Intergroup threat theory further postulates that experiences involving nega-
tive contact are a possible antecedent of perceptions of threat. Given the potential for negative
intergroup contact to increase prejudice against outgroups [40], we hypothesize that videos
concerning ‘Negative Encounters with Older Adults’ will be associated with negative age ste-
reotypes (Hypothesis 1c).
Stereotype content model predicates that the stereotypes applied to social groups fall along
two dimensions: warmth and competence [41]. Seen as occupying a low-status and non-com-
petitive social position, the older demographic is typically perceived as a pitied group com-
posed of warm but incompetent individuals [24, 41]. Hence, we hypothesize that content
portraying older adults as ‘Warm’ will lessen the chances of negative stereotyping (Hypothesis
2).
In undertaking this study, some important points must be raised. Although interrelated,
‘age’ and ‘generation’ are conceptually distinct [42]. Mannheim [43] defined a generation as a
group of people born within a specific period and whose worldviews distinguish them from
other generations. According to Mannheim, one’s generational consciousness arises not sim-
ply from being part of a certain age cohort, but from the experiences that emerge from being
subject to a specific set of social, cultural and historical conditions [44]. Our intention in using
the hashtags #Boomer and #OkBoomer as a vehicle for understanding stereotypes of older per-
sons is not to conflate age and generation. Rather, it is motivated by the possibility that many
people, including the users creating these TikTok videos, are not actually cognizant of the offi-
cial range of birth years—as defined by demographers—of Baby Boomers. Today, the term
‘generation’ is often used interchangeably with age [45]. The chances are therefore high that
some younger people treat ‘Baby Boomers’ as a catch-all term for older adults.
Additionally, the use of generational categories will inevitably have ramifications on how
society makes sense of age and age-related matters [45]. In fact, advocates have recently urged
for the use of generational framing to be abolished [46]. In an ageist society, generational fram-
ing legitimizes segregation on the basis of age and foments ageism [46]. Moreover, while the
phrase ‘Ok, Boomer’ may be a criticism of unprogressive mindsets rather than old age [47], we
argue that the phrase ultimately paints older adults as problematic by singling out a particular
group of individuals. We therefore analyze videos with the hashtags #Boomer and/or #OkBoo-
mer as a way to understand younger people’s stereotypes of older adults, either in general or
those from the Baby Boomer generation. In this study, we use the terms ‘young’ and ‘younger’
to refer to Millennials and members of Generation Z. ‘Old’ and ‘older’ are used to refer to
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users aged 57—the age of the youngest Baby Boomer in 2021 when the data were collected—
and above.
Methods
Dataset
Similar to earlier research [48], a new TikTok account was created to collate the videos. This
was done to reduce bias since videos on the application are sorted based on a complex algo-
rithm which takes into account the popularity of the post (measured by views, likes, comments
and shares), the popularity of the creator (measured by followers and engagement), any previ-
ous content that was engaged with, as well as the geographical location of the device used to
access the application [48]. No content was previously engaged with to guarantee a common
user’s experience in navigating the platform [48].
We compiled publicly available videos (N = 1,937) catalogued under the hashtags #Boomer
and #OkBoomer which garnered a total of 5.4 billion views. Videos were selected based on the
following criteria: (1) Video was publicly available and created by a younger person i.e., videos
created by older adults were excluded; (2) Video was in English; (3) Video featured content
that was relevant to the hashtag i.e., video should be about older adults. All videos had a maxi-
mum duration of one minute as mandated by the platform during the period of the study.
To meet the first inclusion criterion, we processed users’ video frames via Microsoft Azure’s
Face API [49]. The user’s profile picture was used in instances where the user’s face was not
featured in the video. Microsoft Azure’s Face API extracts information regarding age and has
been used in prior research on TikTok [50, 51]. In this study, ‘young’ and ‘younger’ were used
to refer to Millennials and members of Generation Z. The maximum age of a ‘younger’ user
was 40—the age of the oldest Millennial in 2021 when the data were collected—and due to eth-
ical considerations, the minimum age was set as 16. We used ‘old’ and ‘older’ to refer to users
aged 57—the age of the youngest Baby Boomer in 2021—and above. After applying both the
inclusion and exclusion criteria, 673 videos were retained for analysis. Fig 1 depicts a flowchart
of the data collection process.
Ethical considerations
As the videos analyzed in our study were publicly available, we did not seek ethical approval or
consent from the users whose posts were included in our dataset. However, we recognize that
using publicly available data still carries ethical and privacy considerations [52]. Thus, we took
steps to protect the privacy of all users whose videos were included in our study, such as by
removing any identifying information. We also emphasize that our study does not provide any
detailed descriptions of the content of the videos, which means it is difficult to trace the people
who posted the videos. The collection of videos and method of analysis complied with the
terms and conditions of TikTok.
Coding of predictors
Following previous research [53, 54], the codebook (Table 1) was developed through both
inductive and deductive approaches [55]. In inductive content analyses, codes are derived
directly from the data [56]. Meanwhile, analyses led by a directed or deductive approach begin
with the identification of an initial set of codes based on past scholarship [56]. We adopted
both inductive and deductive approaches to ensure that certain pertinent assumptions
informed the analysis while also mindful that new categories would emerge inductively [55].
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To develop a preliminary codebook, we first identified a set of categories based on past liter-
ature. These variables were collated from validated scales measuring age stereotypes as well as
studies regarding perceptions of aging [29, 31]: (1) ‘Physical Functioning’ (e.g., protagonist
comments on the physical abilities of an older person); (2) ‘Appearance’ (e.g., protagonist
comments on the appearance of an older person); (3) ‘Illness/Death’; (4) ‘Warmth’ (i.e.
whether the older person is viewed as warm or cold) [57]; (5) ‘Competence’ (e.g., whether the
older person is viewed as competent or incompetent).
The content analysis was subsequently conducted in several stages, with each video viewed
twice by two researchers trained in gerontology to ensure familiarity with and immersion in
the data [56]. The goal of the first viewing was to confirm the validity of the initial set of catego-
ries, as well as to generate codes systematically across the entire dataset. Each researcher modi-
fied the codebook independently until all variables were refined and clearly defined. During
Fig 1. Process of collating TikTok videos created by young people about older adults.
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the first viewing, a new category was added whenever a video featured a particular attribute
which could not be suitably coded into any of the existing categories and which was recurrent
in the data. During the second viewing, the two coders had frequent discussions during which
any discrepancies were reviewed and adjudicated to ensure rigor in the analysis. At this point,
the two coders discussed what the codes meant, ensured the relevance of the codes to the
research question and identified areas of significant overlap so as to finalize the codebook.
Topics that surfaced prominently across multiple videos and that could not be appropri-
ately coded into the aforementioned categories include: (6) ‘Wealth Gap’ (e.g., protagonist
comments on the existence of a wealth gap between older and younger generations); (7) ‘Val-
ues and Beliefs of Older Adults’ (i.e., protagonist describes older adults as having beliefs that
either violate or uphold cherished values and norms); (8) ‘Negative Encounters with Older
Adults’ (e.g., protagonist describes an unpleasant encounter with an older adult); (9) ‘Older
Adults Antagonizing the Young’ (e.g., protagonist reacts to negative evaluations of younger
people by older adults). With the exception of the ‘Warm/Cold’ and ‘Competent/Incompetent’
variables (Fiske, 2018), both of which were rated on a 5-point scale from 1 (very cold/incompe-
tent) to 5 (very warm/competent), all variables were rated on a binary scale, with the presence
of the attribute in the video rated as 1 and the absence of it rated as 0.
Coding of outcome variable
The outcome variable was ‘Negative Age Stereotypes’. The presence of negative stereotypes
was rated 1 and the absence of it—regardless of whether the stereotype was positive or
Table 1. Codebook for videos created by younger people about older adults on TikTok.
Variable Description Coding
Outcome
Negative Age Stereotypes Whether attitudes displayed by the person in the video reflect any
negative stereotyping of older adults.
0 –Absent
1 –Present
Predictors Coded using Top-down Approach (Variables collated from validated scales measuring age stereotypes
and/or studies regarding perceptions of aging [29, 31]
Physical Functioning Protagonist comments on the physical abilities of an older adult. 0 –Absent
1 –Present
Appearance Protagonist comments on the appearance of an older adult. 0 –Absent
1 –Present
Illness/Death Protagonist talks about an older person in relation to illness or
death.
0 –Absent
1 –Present
Warmth Protagonist describes an older adult as very cold or very warm. 1 –Very cold
5 –Very warm
Competence Protagonist describes an older adult as very incompetent or very
competent (Fiske, 2018).
1 –Very
incompetent
5 –Very
competent
Predictors Coded using Bottom-up Approach (Topics that surfaced prominently across multiple videos and that
could not be appropriately coded into variables identified using the top-down approach)
Wealth Gap Protagonist comments on the existence of a wealth gap between
older and younger generations.
0 –Absent
1 –Present
Values and Beliefs of Older
Adults
Protagonist describes older adults as having beliefs that either
violate or uphold cherished values and norms, whether religious,
political or social.
0 –Absent
1 –Present
Negative Encounters with
Older Adults
Protagonist recounts a prior negative experience with an older
adult.
0 –Absent
1 –Present
Older Adults Antagonizing
the Young
Protagonist reacts to a derogatory comment about younger people
made by an older adult.
0 –Absent
1 –Present
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neutral —was rated 0. An example of a video rated 1 is one that featured a younger person say-
ing that older adults have pillaged the economy. An example of a video rated 0 is one that
described older adults as being kind. Inter-rater reliability was estimated using weighted
Cohen’s kappa for the outcome variable. The percentage agreement between two coders was
97.5% with a weighted Cohen’s kappa of 0.94 (p< .001), indicating high inter-rater reliability.
Analytic strategy
We performed logistic regressions to test our hypotheses. Model 1 included variables extracted
from previous studies on age stereotypes derived from a top-down approach: ‘Physical Func-
tioning’, ‘Appearance’, ‘Illness/Death’, ‘Warmth’ and ‘Competence’. Model 2 consisted of vari-
ables from Model 1 as well as variables derived from a bottom-up approach: ‘Wealth Gap’,
‘Values and Beliefs of Older Adults’, ‘Negative Encounters with Older Adults’ and ‘Older
Adults Antagonizing the Young.’ Analyses were conducted using R version 4.0.3.
Results
Descriptive statistics
Overall, 1 in 2 videos (49.3%) contained negative age stereotypes. Of these videos, 79% were
related to ‘Negative Encounters with Older Adults’, which is more than 4 times the number for
videos without negative age stereotypes. About 58% of the videos included criticisms of the
‘Values and Beliefs of Older Adults’, which is also more than 4 times the number for posts
without negative age stereotypes. About 40% featured ‘Older Adults Antagonizing the Young’,
which was 18 times more than videos without negative age stereotypes. Negatively rated videos
contained more stereotypes of older adults as ‘Cold’. See Table 2 for a summary.
Table 2. Description of topics based on whether videos created by younger people about older adults on TikTok include negative age stereotypes.
Topic Negative Age Stereotypes pb
Absent (N = 341) Present (N = 332)
Physical Functioning Absent 331 (97.1%) 331 (99.7%) 0.02
Present 10 (2.9%) 1 (0.3%)
Appearance Absent 334 (97.9%) 327 (98.5%) 0.81
Present 7 (2.1%) 5 (1.5%)
Illness/Death Absent 323 (94.7%) 314 (94.6%) 1.00
Present 18 (5.3%) 18 (5.4%)
Warmth 2.63 (1.05) 1.39 (0.76) 0.00
Competence 2.82 (0.63) 2.92 (0.39) 0.01
Wealth Gap Absent 335 (98.2%) 309 (93.1%) 0.00
Present 6 (1.8%) 23 (6.9%)
Values and Beliefs of Older Adults Absent 293 (85.9%) 140 (42.2%) 0.00
Present 48 (14.1%) 192 (57.8%)
Negative Encounters with Older Adults Absent 282 (82.7%) 70 (21.1%) 0.00
Present 59 (17.3%) 262 (78.9%)
Older Adults Antagonizing the Young Absent 334 (97.9%) 202 (60.8%) 0.00
Present 7 (2.1%) 130 (39.2%)
a Table values are N and column % for respective variables. b P-value is for t-test (continuous variables) or χ2 test (categorical variables).
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Logistic regression of factors associated with negative age stereotypes on
TikTok
As hypothesized, videos criticizing the ‘Values and Beliefs of Older Adults’ were 7 times more
likely to feature negative age stereotypes (p< .001). Videos about ‘Negative Encounters with
Older Adults’ were 8 times more likely to contain evidence of negative stereotypes of older
adults (p< .001). Content about ‘Older Adults Antagonizing the Young’ was 13 times more
likely to contain negative age stereotypes (p< .001), controlling for other factors (Table 3).
Taken together, these findings provide support for hypotheses 1a through 1c. Meanwhile, vid-
eos portraying older adults as ‘Warm’ were 43% less likely to contain negative stereotypes, pro-
viding support for Hypothesis 2. There was no evidence of multicollinearity as the variance
inflation factor (VIF) scores for all variables were below the conservative threshold of 5.
Discussion
The objective of this study was to analyze the factors linked to negative age stereotypes in vid-
eos created by younger users on TikTok. Findings revealed that these factors include ‘Values
and Beliefs of Older Adults’, ‘Negative Encounters with Older Adults’ and ‘Older Adults
Antagonizing the Young’. Meanwhile, the depiction of older adults as ‘Warm’ was associated
with reduced negative age stereotyping.
In over half of the videos, older adults were stereotyped by younger people as possessing
values and beliefs at odds with those of the latter. This echoes past literature which indicates
that younger persons tend to view their older counterparts as impeding their more progressive
goals related to gender, sexuality and race [3, 22]. By framing their political concerns within
the context of generational differences, these younger users assert their own generational con-
sciousness around various social and political issues.
Many videos featured younger individuals reenacting encounters where they were subject
to derision by older adults because of their youth. In these reenactments, younger people
Table 3. Logistic regression to analyze topics linked to negative age stereotypes in videos created by younger people about older adults on TikTok.
Model (1) (2)
Odds Ratio 95% CI Odds Ratio 95% CI
Physical Functioning 0.31 (0.01–2.52) 0.12 (0.00–4.67)
Appearance 0.68 (0.17–2.54) 0.48 (0.08–2.60)
Illness/Death 1.56 (0.68–3.56) 1.48 (0.46–4.47)
Warmth 0.27*** (0.22–0.33) 0.57*** (0.44–0.74)
Competence 1.08 (0.74–1.60) 1.44 (0.87–2.47)
Wealth Gap 2.90 (0.88–10.16)
Values and Beliefs of Older Adults 7.18*** (4.29–12.36)
Negative Encounters with Older Adults 8.17*** (4.72–14.48)
Older Adults Antagonizing the Young 12.96*** (5.57–34.67)
N: 673 673
AIC: 702.72 496.85
Pseudo R2: 0.26 0.49
Note:
* p< 0.05,
** p< 0.01,
*** p< 0.001.
Constant not shown.
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ranted about how they were frequently typecast as hypersensitive, narcissistic or addicted to
technology. Protzko and Schooler [38] contended that certain cognitive mechanisms grant
individuals a sense of illusory superiority, which consequently prompts them to disparage the
present generation of youth more so than youth from earlier generations. In line with social
identity theory [36, 37], negative attitudes towards older adults may function as a defense
mechanism on the part of younger individuals.
Negative encounters with older adults significantly predicted negative age stereotyping.
Videos on this topic featured younger people attributing the negativity of such encounters to
the fact that these people were part of the older cohort. Consistent with intergroup threat the-
ory, prior negative contact with a particular social group can be a key driver of anxiety towards
that group [39]. Such negative contact may also intensify feelings of prejudice and the propen-
sity to avoid members of a particular group due to the anticipation that future interactions
might likewise be negative [58, 59].
Content portraying older adults as warm reduced the chances of negative age stereotyping.
This reiterates past scholarly claims which suggest that older people are generally stereotyped
as being high in warmth [57]. In these videos, the term ‘Baby Boomer’ was used more as a
demographic label rather than a derogatory term, with younger people admiring older adults
for traits such as friendliness and sincerity.
Practical significance
These findings offer several insights for practitioners. First, more effort should be made to
raise public awareness of ageism as a form of prejudice. This is especially critical in view of
recent findings unveiling that individuals with egalitarian beliefs—specifically those who
champion gender and racial equality—are actually more likely to endorse ageist beliefs [60].
Second, policymakers must pay heed to the fact that unfavorable attitudes towards older adults
may stem from a history of negative contact with them. Since the depiction of older adults as
being warm lessened the chances of negative stereotyping, it is crucial that younger people are
exposed to counter-stereotypical exemplars of older adults, such as those who are more socia-
ble [57]. While stereotypes of warmth may reinforce benevolent ageism [61], they may never-
theless provide a protective buffer against stereotypes of older adults as being antagonistic.
Third, attempts to reframe aging [62–64] as well as to build intergenerational solidarity
should be premised on the understanding that ageism cuts both ways. Just as younger people
should be mindful not to homogenize older adults as sharing the same values and beliefs, effort
should be made to alert older individuals to their own cognitive biases [38] and the effects of
these biases on the way they treat younger people. At present, there is limited literature on
interventions to tackle ageism against younger people [65]. More research in this area should
therefore be conducted to ensure that the needs and interests of younger people are not
neglected in the global campaign to combat ageism [66]. Finally, in order not to divert atten-
tion from more important questions of power and inequality, the media should steer clear of
the notion that certain societal problems, such as climate change or economic strife, can sim-
ply be ascribed to any one age group or generation [46]. Rather than sensationalize differences
between older and younger groups, the media ought to promote a sense of communality by
emphasizing collective goals and cross-generational collaboration [67]. Intergenerational con-
nection will ultimately promote the well-being of both older and younger groups [5].
Limitations and future directions
This study has several limitations. First, the facial recognition software used to determine
users’ ages may have a margin of error [50, 51]. Consequently, some of the videos analyzed
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may not have been created by users belonging to our desired age range. Attitudes towards
older adults could be explored using alternative methods such as interviews, surveys [68–72]
and big data analytics [73–81]. Second, most videos were created by individuals from the
West. Negative age stereotypes may assume a different form in Asia, where the value of filial
piety is generally believed to influence attitudes towards older persons [82–84]. Future
research could consider analyzing content from other platforms like Weibo or Douyin, both of
which are widely used in China. Third, the videos analyzed represent only a fraction of all vid-
eos about older adults on TikTok. The hashtags #Boomer and #OkBoomer were queried
because they were widely used on the platform at the time of analysis. Future studies should
query other search terms to build a more extensive dataset. Fourth, as we did not have infor-
mation regarding users’ intentions for uploading the videos, the conclusions drawn were
heavily dependent on our coding criteria. To minimize any biases, each video was rated inde-
pendently by two researchers—a process which yielded high inter-rater reliability.
Conclusion
With Baby Boomers swelling the ranks of the aging population, 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 pro-
vides 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.
Author Contributions
Conceptualization: Reuben Ng.
Formal analysis: Reuben Ng.
Funding acquisition: Reuben Ng.
Methodology: Reuben Ng.
Supervision: Reuben Ng.
Validation: Reuben Ng.
Visualization: Reuben Ng.
Writing – original draft: Reuben Ng, Nicole Indran.
Writing – review & editing: Reuben Ng, Nicole Indran.
References 1. Bristow J. Stop Mugging Grandma: The “Generation Wars” and Why Boomer Blaming Won’t Solve Any-
thing. Yale University Press; 2019.
2. Martin G, Roberts S. Exploring legacies of the baby boomers in the twenty-first century. The Sociologi-
cal Review. 2021; 00380261211006326. https://doi.org/10.1177/00380261211006326
3. Gonyea JG, Hudson RB. In an Era of Deepening Partisan Divide, What is the Meaning of Age or Gener-
ational Differences in Political Values? Public Policy & Aging Report. 2020; 30: 52–55. https://doi.org/
10.1093/ppar/praa003
4. United States Census Bureau. 2020 Census Will Help Policymakers Prepare for the Incoming Wave of
Aging Boomers. In: Census.gov [Internet]. 10 Dec 2019 [cited 20 Oct 2021]. https://www.census.gov/
library/stories/2019/12/by-2030-all-baby-boomers-will-be-age-65-or-older.html
5. Boger J, Mercer K. Technology for fostering intergenerational connectivity: scoping review protocol.
Systematic Reviews. 2017; 6: 250. https://doi.org/10.1186/s13643-017-0652-y PMID: 29216904
PLOS ONE Videos about older adults on TikTok
PLOS ONE | https://doi.org/10.1371/journal.pone.0285987 August 2, 2023 10 / 14
6. Fishwick S. How to get started on TikTok—an adult’s guide. London Evening Standard. 5 May 2020.
https://www.standard.co.uk/tech/what-is-tiktok-how-to-get-started-a4427206.html. Accessed 3 Feb
2021.
7. Dellatto M. TikTok Hits 1 Billion Monthly Active Users. Forbes. 27 Sep 2021. https://www.forbes.com/
sites/marisadellatto/2021/09/27/tiktok-hits-1-billion-monthly-active-users/. Accessed 21 Oct 2021.
8. Muliadi B. Council Post: What The Rise Of TikTok Says About Generation Z. Forbes. 7 Jul 2020.
https://www.forbes.com/sites/forbestechcouncil/2020/07/07/what-the-rise-of-tiktok-says-about-
generation-z/. Accessed 10 Jul 2021.
9. Ayalon L, Dolberg P, Mikulionienė S, Perek-Białas J, RapolienėG, Stypinska J, et al. A systematic
review of existing ageism scales. Ageing Res Rev. 2019; 54: 100919. https://doi.org/10.1016/j.arr.2019.
100919 PMID: 31228647
10. Ng R, Indran N. Not Too Old for TikTok: How Older Adults Are Reframing Aging. The Gerontologist.
2022; 62: 1207–1216. https://doi.org/10.1093/geront/gnac055 PMID: 35512401
11. Ng R, Indran N. Granfluencers on TikTok: Factors linked to positive self-portrayals of older adults on
social media. PLOS ONE. 2023; 18: e0280281. https://doi.org/10.1371/journal.pone.0280281 PMID:
36749797
12. Cotten SR, Schuster AM, Seifert A. Social media use and well-being among older adults. Curr Opin
Psychol. 2022; 45: 101293. https://doi.org/10.1016/j.copsyc.2021.12.005 PMID: 35065352
13. Ractham P, Techatassanasoontorn A, Kaewkitipong L. Old But Not Out: Social Media Use and Older
Adults’ Life Satisfaction. Australasian Journal of Information Systems. 2022;26. https://doi.org/10.3127/
ajis.v26i0.3269
14. Harriger JA, Thompson JK, Tiggemann M. TikTok, TikTok, the time is now: Future directions in social
media and body image. Body Image. 2023; 44: 222–226. https://doi.org/10.1016/j.bodyim.2023.01.005
PMID: 36739627
15. Levy BR, Chung PH, Bedford T, Navrazhina K. Facebook as a Site for Negative Age Stereotypes. The
Gerontologist. 2014; 54: 172–176. https://doi.org/10.1093/geront/gns194 PMID: 23392643
16. Jimenez-Sotomayor MR, Gomez-Moreno C, Soto-Perez-de-Celis E. Coronavirus, Ageism, and Twitter:
An Evaluation of Tweets about Older Adults and COVID-19. Journal of the American Geriatrics Society.
2020; 68: 1661–1665. https://doi.org/10.1111/jgs.16508 PMID: 32338787
17. Skipper AD, Rose DJ. #BoomerRemover: COVID-19, ageism, and the intergenerational twitter
response. Journal of Aging Studies. 2021; 57: 100929. https://doi.org/10.1016/j.jaging.2021.100929
PMID: 34082999
18. Ng R, Indran N, Liu L. Ageism on Twitter during the COVID-19 pandemic. Journal of Social Issues.
2022; 78: 842–859. https://doi.org/10.1111/josi.12535 PMID: 35942488
19. Xiang X, Lu X, Halavanau A, Xue J, Sun Y, Lai PHL, et al. Modern Senicide in the Face of a Pandemic:
An Examination of Public Discourse and Sentiment About Older Adults and COVID-19 Using Machine
Learning. The Journals of Gerontology: Series B. 2021; 76: e190–e200. https://doi.org/10.1093/geronb/
gbaa128 PMID: 32785620
20. Xi W, Xu W, Zhang X, Ayalon L. A Thematic Analysis of Weibo Topics (Chinese Twitter Hashtags)
Regarding Older Adults During the COVID-19 Outbreak. J Gerontol B Psychol Sci Soc Sci. 2021; 76:
e306–e312. https://doi.org/10.1093/geronb/gbaa148 PMID: 32882029
21. Ng R, Chow TYJ, Yang W. Culture Linked to Increasing Ageism during Covid-19: Evidence from a 10-
billion-word Corpus across 20 Countries. The Journals of Gerontology: Series B. 2021; 76: 1808–1816.
https://doi.org/10.1093/geronb/gbab057 PMID: 33786581
22. Ng R, Indran N. Hostility Toward Baby Boomers on TikTok. The Gerontologist. 2022; 62: 1196–1206.
https://doi.org/10.1093/geront/gnac020 PMID: 35106587
23. Butler RN. Age-Ism: Another Form of Bigotry. The Gerontologist. 1969; 9: 243–246. https://doi.org/10.
1093/geront/9.4_part_1.243 PMID: 5366225
24. North MS, Fiske ST. An Inconvenienced Youth? Ageism and its Potential Intergenerational Roots. Psy-
chol Bull. 2012; 138. https://doi.org/10.1037/a0027843 PMID: 22448913
25. Ng R. Societal Age Stereotypes in the U.S. and U.K. from a Media Database of 1.1 Billion Words. Inter-
national Journal of Environmental Research and Public Health. 2021; 18: 8822. https://doi.org/10.3390/
ijerph18168822 PMID: 34444578
26. Ng R, Lim SQ, Saw SY, Tan KB. 40-Year Projections of Disability and Social Isolation of Older Adults
for Long-Range Policy Planning in Singapore. International Journal of Environmental Research and
Public Health. 2020; 17: 4950. https://doi.org/10.3390/ijerph17144950 PMID: 32659983
27. Ng R, Indran N, Yang W. Portrayals of older adults in over 3000 films around the world. Journal of the
American Geriatrics Society. 2023. https://doi.org/10.1111/jgs.18400 PMID: 37314013
PLOS ONE Videos about older adults on TikTok
PLOS ONE | https://doi.org/10.1371/journal.pone.0285987 August 2, 2023 11 / 14
28. Hummert ML. Multiple Stereotypes of Elderly and Young Adults: A Comparison of Structure and Evalua-
tions. Psychology and Aging. 1990; 5: 182–193. https://doi.org/10.1037//0882-7974.5.2.182 PMID:
2378684
29. Schmidt DE, Boland SM. Structure of Perceptions of Older Adults: Evidence for Multiple Stereotypes.
Psychology and Aging. 1986; 1: 255–260. https://doi.org/10.1037//0882-7974.1.3.255 PMID: 3267406
30. Casper C, Rothermund K, Wentura D. The Activation of Specific Facets of Age Stereotypes Depends
on Individuating Information. Social Cognition. 2011; 29: 393–414. https://doi.org/10.1521/soco.2011.
29.4.393
31. Ng R, Allore HG, Trentalange M, Monin JK, Levy BR. Increasing Negativity of Age Stereotypes across
200 Years: Evidence from a Database of 400 Million Words. PLOS ONE. 2015; 10: e0117086. https://
doi.org/10.1371/journal.pone.0117086 PMID: 25675438
32. Ng R, Chow TYJ. Aging Narratives over 210 years (1810–2019). The Journals of Gerontology: Series
B. 2021; 76: 1799–1807. https://doi.org/10.1093/geronb/gbaa222 PMID: 33300996
33. Levy B. Stereotype Embodiment. Curr Dir Psychol Sci. 2009; 18: 332–336.
34. Ayalon L, Tesch-Römer C. Introduction to the Section: Ageism—Concept and Origins. In: Ayalon L,
Tesch-Römer C, editors. Contemporary Perspectives on Ageism. Cham: Springer International Pub-
lishing; 2018. pp. 1–10.
35. Tajfel H. Social identity and intergroup behaviour. Social Science Information. 1974; 13: 65–93. https://
doi.org/10.1177/053901847401300204
36. Tajfel H, Turner JC. The Social Identity Theory of Intergroup Behavior. 2nd ed. In: Worchel S, Austin
WG, editors. Psychology of intergroup relations. 2nd ed. Chicago: Nelson-Hall; 1985. pp. 7–24.
37. Branscombe NR, Ellemers N, Spears R, Doosje B. The context and content of social identity threat. In:
Ellemers E, Spears R, Doosje B, editors. Social identity: Context, commitment, content. Oxford,
England: Blackwell Science; 1999. pp. 35–58.
38. Protzko J, Schooler J. Kids these days: Why the youth of today seem lacking. Science Advances. 2019;
5: eaav5916. https://doi.org/10.1126/sciadv.aav5916 PMID: 31663012
39. Stephan W, Ybarra O, Rios K. Intergroup Threat Theory. In: Nelson T, editor. Handbook of Prejudice.
Mahwah, NJ: Lawrence Elbaum Associates; 2009. pp. 43–59.
40. Meleady R, Forder L. When contact goes wrong: Negative intergroup contact promotes generalized out-
group avoidance. Group Processes & Intergroup Relations. 2019; 22: 688–707. https://doi.org/10.1177/
1368430218761568
41. Fiske ST, Cuddy AJC, Glick P, Xu J. A model of (often mixed) stereotype content: Competence and
warmth respectively follow from perceived status and competition. Journal of Personality and Social
Psychology. 2002; 82: 878–902. https://doi.org/10.1037/0022-3514.82.6.878 PMID: 12051578
42. Meinich J, Sang K. “It does not help to look young and dumb. . .”: Ageism and intergenerational contact
in the Norwegian technical industry. Equality, Diversity and Inclusion: An International Journal. 2018;
37: 202–218. https://doi.org/10.1108/EDI-04-2017-0089
43. Mannheim K. The Problem of Generations. In: Kecskemeti P, editor. Essays on the Sociology of Knowl-
edge. London: Routledge and Kegan Paul; 1952. pp. 276–320. http://marcuse.faculty.history.ucsb.
edu/classes/201/articles/27MannheimGenerations.pdf
44. Purhonen S. Generations on paper: Bourdieu and the critique of “generationalism.” Social Science
Information. 2016; 55: 94–114. https://doi.org/10.1177/0539018415608967
45. Pritchard K, Whiting R. Baby Boomers and the Lost Generation: On the Discursive Construction of Gen-
erations at Work. Organization Studies. 2014; 35: 1605–1626. https://doi.org/10.1177/
0170840614550732
46. Applewhite A. Let’s Climb Out of the Generation Trap. Next Avenue. 29 Jun 2021. https://www.
nextavenue.org/generation-trap/. Accessed 31 Oct 2021.
47. Fabris J. “OK, boomer” attacks a mindset, not a generation. The Occidental News. 19 Nov 2019. https://
www.theoccidentalnews.com/opinions/2019/11/19/ok-boomer-attacks-a-mindset-not-a-generation/
2899726. Accessed 2 Nov 2021.
48. Herrick SSC, Hallward L, Duncan LR. “This is just how I cope”: An inductive thematic analysis of eating
disorder recovery content created and shared on TikTok using #EDrecovery. International Journal of
Eating Disorders. 2020; 54: 516–526. https://doi.org/10.1002/eat.23463 PMID: 33382136
49. Face API: An AI service that analyzes faces in images. In: Microsoft Azure [Internet]. 2021 [cited 4 Nov
2021]. https://azure.microsoft.com/en-us/services/cognitive-services/face/
50. Fiallos A, Fiallos C, Figueroa S. TikTok and Education: Discovering Knowledge through Learning Vid-
eos. 2021 Eighth International Conference on eDemocracy & eGovernment (ICEDEG). Quito, Ecuador:
IEEE; 2021. pp. 172–176.
PLOS ONE Videos about older adults on TikTok
PLOS ONE | https://doi.org/10.1371/journal.pone.0285987 August 2, 2023 12 / 14
51. Serrano JCM, Papakyriakopoulos O, Hegelich S. Dancing to the Partisan Beat: A First Analysis of Politi-
cal Communication on TikTok. 12th ACM Conference on Web Science. Southampton United Kingdom:
ACM; 2020. pp. 257–266.
52. Stommel W, de Rijk L. Ethical approval: none sought. How discourse analysts report ethical issues
around publicly available online data. Research Ethics. 2021; 17: 275–297. https://doi.org/10.1177/
1747016120988767
53. Sipocz D, Freeman JD, Elton J. “A Toxic Trend?”: Generational Conflict and Connectivity in Twitter Dis-
course Under the #BoomerRemover Hashtag. Bowers BJ, editor. The Gerontologist. 2021; 61: 166–
175. https://doi.org/10.1093/geront/gnaa177 PMID: 33159524
54. Ng R, Indran N. Age Advocacy on Twitter over 12 years. The Gerontologist. 2022; gnac183. https://doi.
org/10.1093/geront/gnac183 PMID: 36516463
55. Armat MR, Assarroudi A, Rad M, Sharifi H, Heydari A. Inductive and Deductive: Ambiguous Labels in
Qualitative Content Analysis. The Qualitative Report. 2018; 23: 219–221. https://doi.org/10.46743/
2160-3715/2018.2872
56. Hsieh H-F, Shannon SE. Three Approaches to Qualitative Content Analysis. Qual Health Res. 2005;
15: 1277–1288. https://doi.org/10.1177/1049732305276687 PMID: 16204405
57. Fiske ST. Stereotype Content: Warmth and Competence Endure. Curr Dir Psychol Sci. 2018; 27: 67–
73. https://doi.org/10.1177/0963721417738825 PMID: 29755213
58. Barlow FK, Paolini S, Pedersen A, Hornsey MJ, Radke HRM, Harwood J, et al. The Contact Caveat:
Negative Contact Predicts Increased Prejudice More Than Positive Contact Predicts Reduced Preju-
dice. Pers Soc Psychol Bull. 2012; 38: 1629–1643. https://doi.org/10.1177/0146167212457953 PMID:
22941796
59. Hayward LE, Tropp LR, Hornsey MJ, Barlow FK. Toward a Comprehensive Understanding of Inter-
group Contact: Descriptions and Mediators of Positive and Negative Contact Among Majority and
Minority Groups. Pers Soc Psychol Bull. 2017; 43: 347–364. https://doi.org/10.1177/
0146167216685291 PMID: 28903695
60. Martin AE, North MS. Equality for (almost) all: Egalitarian advocacy predicts lower endorsement of sex-
ism and racism, but not ageism. Journal of Personality and Social Psychology. 2021 [cited 1 Aug 2021].
https://doi.org/10.1037/pspi0000262 PMID: 33464112
61. Cary LA, Chasteen AL, Remedios J. The Ambivalent Ageism Scale: Developing and Validating a Scale
to Measure Benevolent and Hostile Ageism. The Gerontologist. 2017; 57: e27–e36. https://doi.org/10.
1093/geront/gnw118 PMID: 27520730
62. Ng R, Indran N. Role-Based Framing of Older Adults Linked to Decreased Ageism Over 210 Years: Evi-
dence From a 600-Million-Word Historical Corpus. The Gerontologist. 2022; 62: 589–597. https://doi.
org/10.1093/geront/gnab108 PMID: 34323967
63. Ng R, Indran N. Reframing aging during COVID-19: Familial role-based framing of older adults linked to
decreased ageism. Journal of the American Geriatrics Society. 2022; 70: 60–66. https://doi.org/10.
1111/jgs.17532 PMID: 34674224
64. Ng R, Indran N, Liu L. A playbook for effective age advocacy on Twitter. Journal of the American Geriat-
rics Society. 2022; 70: 2363–2370. https://doi.org/10.1111/jgs.17909 PMID: 35748402
65. de la Fuente-Núñez V, Cohn-Schwartz E, Roy S, Ayalon L. Scoping Review on Ageism against Youn-
ger Populations. Int J Environ Res Public Health. 2021; 18: 3988. https://doi.org/10.3390/
ijerph18083988 PMID: 33920114
66. de la Fuente-Núñez V, Officer A, Demographic Change and Healthy Ageing Unit. The Global Campaign
to Combat Ageism calls on us to act together. Nature Aging. 2021; 1: 146–146. https://doi.org/10.1038/
s43587-021-00036-4 PMID: 37118635
67. Burke RJ, Richardsen AM. Creating Psychologically Healthy Workplaces. Edward Elgar Publishing;
2019.
68. El-Muttardi N, Lancaster K, Ng R, Mercer D. The sandwich omental flap for abdominal wall defect
reconstruction. Br J Plast Surg. 2005; 58: 841–844. https://doi.org/10.1016/j.bjps.2004.12.031 PMID:
16086992
69. Ng R, Allore HG, Monin JK, Levy BR. Retirement as Meaningful: Positive Retirement Stereotypes Asso-
ciated with Longevity. J Soc Issues. 2016; 72: 69–85. https://doi.org/10.1111/josi.12156 PMID:
27346893
70. Ng R, Allore HG, Levy BR. Self-Acceptance and Interdependence Promote Longevity: Evidence From
a 20-year Prospective Cohort Study. International Journal of Environmental Research and Public
Health. 2020; 17: 5980. https://doi.org/10.3390/ijerph17165980 PMID: 32824658
71. Ng R, Levy B. Pettiness: Conceptualization, measurement and cross-cultural differences. PLOS ONE.
2018; 13: e0191252. https://doi.org/10.1371/journal.pone.0191252 PMID: 29385157
PLOS ONE Videos about older adults on TikTok
PLOS ONE | https://doi.org/10.1371/journal.pone.0285987 August 2, 2023 13 / 14
72. Ng R, Rayner S. Integrating psychometric and cultural theory approaches to formulate an alternative
measure of risk perception. Innovation: The European Journal of Social Science Research. 2010; 23:
85–100. https://doi.org/10.1080/13511610.2010.512439
73. Giest S, Ng R. Big Data Applications in Governance and Policy. Politics and Governance. 2018; 6: 1–4.
https://doi.org/10.17645/pag.v6i4.1810
74. Ng R, Tan KB. Implementing an Individual-Centric Discharge Process across Singapore Public Hospi-
tals. International Journal of Environmental Research and Public Health. 2021; 18: 8700. https://doi.org/
10.3390/ijerph18168700 PMID: 34444448
75. Ng R, Chow TYJ, Yang W. News media narratives of Covid-19 across 20 countries: Early global conver-
gence and later regional divergence. PLOS ONE. 2021; 16: e0256358. https://doi.org/10.1371/journal.
pone.0256358 PMID: 34469446
76. Ng R, Chow TYJ, Yang W. The Impact of Aging Policy on Societal Age Stereotypes and Ageism. The
Gerontologist. 2022; 62: 598–606. https://doi.org/10.1093/geront/gnab151 PMID: 34636402
77. Ng R, Tan YW. Diversity of COVID-19 News Media Coverage across 17 Countries: The Influence of
Cultural Values, Government Stringency and Pandemic Severity. International Journal of Environmen-
tal Research and Public Health. 2021; 18: 11768. https://doi.org/10.3390/ijerph182211768 PMID:
34831524
78. Ng R, Tan YW. Media attention toward COVID-19 across 18 countries: The influence of cultural values
and pandemic severity. PLOS ONE. 2022; 17: e0271961. https://doi.org/10.1371/journal.pone.
0271961 PMID: 36477463
79. Ng R. Anti-Asian Sentiments During the COVID-19 Pandemic Across 20 Countries: Analysis of a 12-Bil-
lion-Word News Media Database. Journal of Medical Internet Research. 2021; 23: e28305. https://doi.
org/10.2196/28305 PMID: 34678754
80. Ng R, Indran N. Impact of Old Age on an Occupation’s Image Over 210 Years: An Age Premium for
Doctors, Lawyers, and Soldiers. J Appl Gerontol. 2023; 42: 1345–1355. https://doi.org/10.1177/
07334648231155025 PMID: 37092180
81. Ng R, Indran N. Innovations for an Aging Society through the Lens of Patent Data. The Gerontologist.
2023.
82. Ng R, Indran N. Societal Narratives on Caregivers in Asia. International Journal of Environmental
Research and Public Health. 2021; 18: 11241. https://doi.org/10.3390/ijerph182111241 PMID:
34769759
83. Ng R, Indran N. Societal perceptions of caregivers linked to culture across 20 countries: Evidence from
a 10-billion-word database. PLOS ONE. 2021; 16: e0251161. https://doi.org/10.1371/journal.pone.
0251161 PMID: 34197470
84. Ng R, Lim-Soh JW. Ageism Linked to Culture, Not Demographics: Evidence From an 8-Billion-Word
Corpus Across 20 Countries. The Journals of Gerontology: Series B. 2021; 76: 1791–1798. https://doi.
org/10.1093/geronb/gbaa181 PMID: 33099600
PLOS ONE Videos about older adults on TikTok
PLOS ONE | https://doi.org/10.1371/journal.pone.0285987 August 2, 2023 14 / 14
