Introduction: Individuals’ construals of aging capture how they think of aging, and what aging well means to them. Assessing such construals is important for understanding attitudes toward aging and, ultimately, how to tailor personalized aging well interventions to an individual. Methods: We analyzed 100 younger adults (YAs)’ and 92 older adults (OAs)’ spoken narratives of what aging well means to them using two language analysis approaches, closed-vocabulary, word count analysis via Linguistic Inquiry and Word Count (LIWC) and open-vocabulary, word co-occurrence analysis via topic modeling. Results: YAs’ and OAs’ spoken narratives differed in both word and topic use. YAs used more words related to physical aspects, more tentative language, and expressed themselves via higher status language (clout), while OAs used authentic language, i-talk, and words related to work, home, family, and religion. Topic modeling complemented the LIWC analyses and showed that YAs primarily discussed topics of bodily and cognitive decline and strategies of preventing aging, conveying concerns about, and negative stereotypes of aging. OAs topicalized family reflections, openness to new experiences, and their social engagement, signaling a more positive outlook on (continued) aging. Conclusion: Our complimentary word count and word co-occurrence language analyses of aging well construals revealed stark differences between YAs’ and OAs’ perceptions of aging well, which raise important questions about intergenerational exchanges and communications about aging more broadly. Further, we found that aging construals of OAs are useful for estimating their future outlook, an important aspect of resilience against cognitive decline and possible entry point for targeted precision aging interventions.

1.
Ryan
L
,
Hay
M
,
Huentelman
MJ
,
Duarte
A
,
Rundek
T
,
Levin
B
, et al
.
Precision aging: applying precision medicine to the field of cognitive aging
.
Front Aging Neurosci
.
2019
;
11
:
128
.
2.
Meier
T
,
Mehl
MR
,
Martin
M
,
Horn
AB
.
When I am sixty-fourWhen I am sixty-four… evaluating language markers of well-being in healthy aging narrativesevaluating language markers of well-being in healthy aging narratives
.
PLoS One
.
2024
;
19
(
4
):
e0302103
.
3.
Bryant
LL
,
Corbett
KK
,
Kutner
JS
.
In their own words: a model of healthy aging
.
Soc Sci Med
.
2001
;
53
(
7
):
927
41
.
4.
Brown
KE
,
Kim
J
,
Stewart
T
,
Fulton
E
,
McCarrey
AC
.
Positive, but not negative, self-perceptions of aging predict cognitive function among older adults
.
Int J Aging Hum Dev
.
2021
;
93
(
1
):
543
61
.
5.
Boyd
RL
,
Markowitz
DM
.
Verbal behavior and the future of social science
.
Am Psychol
.
2024
.
6.
Dehghani
M
,
Boyd
RL
, editors.
Handbook of language analysis in psychology
.
Guilford Publications
;
2022
.
7.
Mihalcea
R
,
Biester
L
,
Boyd
RL
,
Jin
Z
,
Perez-Rosas
V
,
Wilson
S
, et al
.
How developments in natural language processing help us in understanding human behaviour
.
Nat Hum Behav
.
2024
;
8
(
10
):
1877
89
.
8.
Boyd
RL
,
Ashokkumar
A
,
Seraj
S
,
Pennebaker
JW
.
The development and psychometric properties of LIWC-22
.
Austin, TX
:
University of Texas at Austin
;
2022
. Vol.
10
; p.
1
47
.
9.
Pennebaker
JW
,
Boyd
RL
,
Booth
RJ
,
Ashokkumar
A
,
Francis
ME
.
Linguistic inquiry and word count: liwc-22. pennebaker conglomerates
.
2022
.
10.
Tackman
AM
,
Sbarra
DA
,
Carey
AL
,
Donnellan
MB
,
Horn
AB
,
Holtzman
NS
, et al
.
Depression, negative emotionality, and self-referential language: a multi-lab, multi-measure, and multi-language-task research synthesis
.
J Pers Soc Psychol
.
2019
;
116
(
5
):
817
34
.
11.
Chung
CK
,
Pennebaker
JW
.
What do we know when we LIWC a person? Text analysis as an assessment tool for traits, personal concerns and life stories
.
The Sage handbook Personal individual differences
.
2018
:
341
60
.
12.
Sun
J
,
Harris
K
,
Vazire
S
.
Is well-being associated with the quantity and quality of social interactions
.
J Pers Soc Psychol
.
2020
;
119
(
6
):
1478
96
.
13.
Pennebaker
JW
,
Stone
LD
.
Words of wisdom: language use over the life span
.
J Pers Soc Psychol
.
2003
;
85
(
2
):
291
301
.
14.
Roberts
ME
,
Stewart
BM
,
Tingley
D
,
Lucas
C
,
Leder-Luis
J
,
Gadarian
SK
, et al
.
Structural topic models for open‐ended survey responses
.
Am J Pol Sci
.
2014
;
58
(
4
):
1064
82
.
15.
Markowitz
DM
.
The meaning extraction method:an approach to evaluate content patterns from large-scale language data
.
Front Commun
.
2021
;
6
:
588823
.
16.
Eichstaedt
JC
,
Kern
ML
,
Yaden
DB
,
Schwartz
HA
,
Giorgi
S
,
Park
G
, et al
.
Closed-and open-vocabulary approaches to text analysis: a review, quantitative comparison, and recommendations
.
Psychol Methods
.
2021
;
26
(
4
):
398
427
.
17.
Landauer
TK
,
Foltz
PW
,
Laham
D
.
An introduction to latent semantic analysis
.
Discourse Process
.
1998
;
25
(
2–3
):
259
84
.
18.
Blei
DM
,
Ng
AY
,
Jordan
MI
.
Latent dirichlet allocation
.
J machine Learn Res
.
2003
;
3
(
Jan
):
993
1022
.
19.
Gupta
SS
,
Jordan
KN
.
Does the gender of doctors change a patient’s perception
.
Health Commun
.
2025
;
40
(
2
):
258
67
.
20.
Kern
ML
,
Eichstaedt
JC
,
Schwartz
HA
,
Park
G
,
Ungar
LH
,
Stillwell
DJ
, et al
.
From “Sooo excited!!!” to “So proud”: using language to study development
.
Dev Psychol
.
2014
;
50
(
1
):
178
88
.
21.
Makita
M
,
Mas-Bleda
A
,
Stuart
E
,
Thelwall
M
.
Ageing, old age and older adults: a social media analysis of dominant topics and discourses
.
Ageing Soc
.
2021
;
41
(
2
):
247
72
.
22.
Kim
HN
,
Freddolino
PP
.
Topic clusters of successful aging studies: results of a topic modeling approach
.
Gerontologist
.
2024
;
65
(
1
):
gnae095
.
23.
Wang
L
,
Lakin
J
,
Riley
C
,
Korach
Z
,
Frain
LN
,
Zhou
L
.
Disease trajectories and end-of-life care for dementias: latent topic modeling and trend analysis using clinical notes
.
AMIA Annu Symp Proc
.
2018
;
2018
:
1056
65
.
24.
Laserna
CM
,
Seih
YT
,
Pennebaker
JW
.
Um. who like says you know: filler word use as a function of age, gender, and personality
.
J Lang Soc Psychol
.
2014
;
33
(
3
):
328
38
.
25.
Pfeifer
VA
,
Chilton
TD
,
Grilli
MD
,
Mehl
MR
.
How ready is speech-to-text for psychological language research? Evaluating the validity of AI-generated English transcripts for analyzing free-spoken responses in younger and older adults
.
Behav Res Methods
.
2024
;
56
(
7
):
7621
31
.
26.
Roberts
ME
,
Stewart
BM
,
Tingley
D
.
Stm: an R package for structural topic models
.
J Stat Softw
.
2019
;
91
(
2
):
1
40
.
27.
Wickham
H
,
Averick
M
,
Bryan
J
,
Chang
W
,
McGowan
LD
,
François
R
, et al
.
Welcome to the tidyverse
.
J Open Source Softw
.
2019
;
4
(
43
):
1686
.
28.
Carstensen
LL
,
Isaacowitz
DM
,
Charles
ST
.
Taking time seriously: a theory of socioemotional selectivity
.
Am Psychol
.
1999
;
54
(
3
):
165
81
.
29.
Sakar
M
,
Parihar
J
,
Gupta
L
.
Unequal aging: disparities in geriatric research and A call for unified action
.
Anti-Aging East Europe
.
2024
;
3
(
3
):
114
8
.
30.
Jarrott
SE
.
Programs that affect intergenerational solidarity
.
Intergenerational solidarity: strengthening economic and social ties
.
New York
:
Palgrave Macmillan US
;
2010
. p.
113
27
.
31.
Webster
M
,
Norwood
K
,
Waterworth
J
,
Leavey
G
.
Effectiveness of intergenerational exchange programs between adolescents and older adults: a systematic review
.
J Intergenerational Relationships
.
2024
;
22
(
4
):
603
44
.
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