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The rise and fall of rationality in language
Marten Scheffer
a,1
, Ingrid van de Leemput
a
, Els Weinans
a,b
, and Johan Bollen
c,1
a
Department of Environmental Sciences, Wageningen University, 6700 AA Wageningen, The Netherlands;
b
Department of Industrial Engineering and
Innovation Sciences,Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; and
c
Department of Informatics, Cognitive Science Program,
Indiana University, Bloomington, IN 47408
Contributed by Marten Scheffer; received October 25, 2021; accepted November 2, 2021; reviewed by Simon DeDeo, Maximilian Schich, and Peter Sloot
The surge of post-truth political argumentation suggests that we
are living in a special historical period when it comes to the bal-
ance between emotion and reasoning. To explore if this is indeed
the case, we analyze language in millions of books covering the
period from 1850 to 2019 represented in Google nGram data. We
show that the use of words associated with rationality, such as
“determine”and “conclusion,”rose systematically after 1850,
while words related to human experience such as “feel”and
“believe”declined. This pattern reversed over the past decades,
paralleled by a shift from a collectivistic to an individualistic focus
as reflected, among other things, by the ratio of singular to plural
pronouns such as “I”/”we”and “he”/”they.”Interpreting this syn-
chronous sea change in book language remains challenging. How-
ever, as we show, the nature of this reversal occurs in fiction as
well as nonfiction. Moreover, the pattern of change in the ratio
between sentiment and rationality flag words since 1850 also
occurs in New York Times articles, suggesting that it is not an arti-
fact of the book corpora we analyzed. Finally, we show that word
trends in books parallel trends in corresponding Google search
terms, supporting the idea that changes in book language do in
part reflect changes in interest. All in all, our results suggest that
over the past decades, there has been a marked shift in public
interest from the collective to the individual, and from rationality
toward emotion.
language jrationality jsentiment jcollectivity jindividuality
The post-truth era where “feelings trump facts” (1) may seem
special when it comes to the historical balance between emo-
tion and reasoning. However, quantifying this intuitive notion
remains difficult as systematic surveys of public sentiment and
worldviews do not have a very long history. We address this gap
by systematically analyzing word use in millions of books in
English and Spanish covering the period from 1850 to 2019 (2).
Reading this amount of text would take a single person millennia,
but computational analyses of trends in relative word frequencies
may hint at aspects of cultural change (2–4). Print culture is
selective and cannot be interpreted as a straightforward reflection
of culture in a broader sense (5). Also, the popularity of particu-
lar words and phrases in a language can change for many reasons
including technological context (e.g., carriage or computer), and
themeaningofsomewordscanchangeprofoundlyovertime
(e.g., gay) (6). Nonetheless, across large amounts of words, pat-
terns of change in frequencies may to some degree reflect
changes in the way people feel and see the world (2–4), assuming
that concepts that are more abundantly referred to in books in
part represent concepts that readers at that time were more
interested in. Here, we systematically analyze long-term dynamics
in the frequency of the 5,000 most used words in English and
Spanish (7) in search of indicators of changing world views. We
also analyze patterns in fiction and nonfiction separately. More-
over, we compare patterns for selected key words in other lan-
guages to gauge the robustness and generalizability of our results.
To see if results might be specific to the corpora of book language
we used, we analyzed how word use changed in the New York
Times since 1850. In addition, to probe whether changes in the
frequency of words used in books does indeed reflect interest in
the corresponding concepts we analyzed how change in Google
word searches relates to the recent change in words used in
books. Following best-practice guidelines (8) we standardized
word frequencies by dividing them by the frequency of the word
“an,” which is indicative of total text volume, and subsequently
taking z-scores (SI Appendix,sections1,5,and8).
Principal Components of Change
Analyzing language change can imply the risk of cherry-picking.
Therefore, as a first unbiased exploration, we perform a principal
component analysis (PCA) on the z-scores of relative word fre-
quencies in books over time (SI Appendix,section2). This
approach seeks to capture patterns of change in a large dataset
without relying on prior assumptions or search images. In both
Spanish and English, the first principal component (PC1) corre-
sponds to a monotonic trend over time. The second principal
component (PC2) shows an asymmetric U-curve, or “tilted
hockeystick,” declining gradually since the industrial revolution
and surging sharply in recent decades (Fig. 1, first column). Exami-
nation of the words that score highly on opposite ends of either
principal component axis (Table 1 and SI Appendix,section10)
suggests that in both English and Spanish the monotonic axis cap-
tures general trends of word popularity over time. On the high
end (representing earlier times) we find more archaic terms such
as civilized,ox,straw,savage,carriage,andsheriff. On the low end
(corresponding to more recent times) we find words such as cola,
product,ski,allergic,tech,anddummy. By contrast, the tilted hock-
eystick axes are dominated on the high side (more recently) by
words reflecting concepts related to personal experience such as
senses, spirituality, emotions, and personal relationships as detailed
Significance
The post-truth era has taken many by surprise. Here, we use
massive language analysis to demonstrate that the rise of fact-
free argumentation may perhaps be understood as part of a
deeper change. After the year 1850, the use of sentiment-
laden words in Google Books declined systematically, while
the use of words associated with fact-based argumentation
rose steadily. This pattern reversed in the 1980s, and this
change accelerated around 2007, when across languages, the
frequency of fact-related words dropped while emotion-laden
language surged, a trend paralleled by a shift from collectivistic
to individualistic language.
Author contributions: M.S., I.v.d.L., E.W., and J.B. designed research; I.v.d.L., E.W., and
J.B. performed research; I.v.d.L. and E.W. analyzed data; and M.S., I.v.d.L., E.W., and
J.B. wrote the paper.
Reviewers: S.D., Carnegie Mellon University; M.S., Tallinna Ulikool; and P.S.,
Universiteit van Amsterdam.
The authors declare no competing interest.
This open access article is distributed under Creative Commons Attribution License 4.0
(CC BY).
See online for related content such as Commentaries.
1
To whom correspondence may be addressed. Email: marten.scheffer@wur.nl or
jbollen@indiana.edu.
This article contains supporting information online at http://www.pnas.org/lookup/
suppl/doi:10.1073/pnas.2107848118/-/DCSupplemental.
Published December 16, 2021.
PNAS 2021 Vol. 118 No. 51 e2107848118 https://doi.org/10.1073/pnas.2107848118 j
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later (Table 1). By contrast, the opposite end of this axis has words
related to society including words associated with science, rational
decision-making, procedures, and systems (Table 1).
Sentiment Trends
As a next objective step, we analyzed changes in the relative
frequencies of words that have been independently assessed as
indicators of different aspects of emotion (using the ANEW
[Affective Norms for English Words] lexicon) ( 9) and a compa-
rable lexicon for Spanish (see SI Appendix, section 3).
“Valence” or pleasantness associated with a word is a dominant
aspect of many models of human emotion. ANEW valence
values range from low (e.g., for words such as torture,rape,ter-
rorism) to high (e.g., words such as vacation,enjoyment,free).
Some models of human emotions also include an orthogonal
affective dimension of arousal, which can be evoked by words
going from low arousal (e.g., dull,scene,asleep) to high arousal
(e.g., rampage,sex,tornado). Integrating frequency-weighted
valence and arousal sentiment scores for all words from our set
of 5,000 that were listed in the sentiment lexicons we arrive at
an index of positive and negative sentiment as well as arousal
for the entire content of books published each year since 1850.
In both English and Spanish we find patterns of these affective
indicators that closely resemble the hockeystick patterns of the
PCA (Fig. 1, second column and SI Appendix, Table S1).
Principal Component
English All
A
Sentiment
B
Intuition related words
C
Rationality related words
D
Spanish
E F G H
English Fiction
I J K L
1850
1875
1900
1925
1950
1975
2000
2025
year
English excl Fiction
M
1850
1875
1900
1925
1950
1975
2000
2025
year
N
1850
1875
1900
1925
1950
1975
2000
2025
year
O
1850
1875
1900
1925
1950
1975
2000
2025
year
P
Fig. 1. Dynamics of four characteristics of English and Spanish book language represented in Google n-gram data. (A,E,I,andM) Second principal compo-
nent of change in z-scores of frequencies of the 5,000 most-used words. (B,F,J, and N) Relative level of arousal (black), positive sentiment (blue), and nega-
tive sentiment (red). (C,G,K,andO) Z-scores of f requencies of flag-words related to intuition, believing, spirituality, sapience: spirit,imagine,wisdom,wise,
hunch,mind,suspicion,believe,think,trust,faith,truth,true,belief,doubt,hope,fear,life,soul,heaven,eternal,mortal,holy,god,pray,mystery,sense,
feel,soft,hard,cold,hot,smell,foul,taste,sweet,bitter,hear,sound,silence,loud,see,light,dark,bright (for Spanish: esp
ıritu,imaginar,sabidur
ıa,mente,
sospecha,creer,pensar,fe,verdad,duda,esperanza,miedo,vida,alma,cielo,santo,dios,misterio,sentido,sensaci
on,sentir,suave,duro,fr
ıo,caliente,
gusto,dulce,o
ır,silencio,fuerte,ver,mirar,oscuro,brillante). The black central line represents the mean and the gray shaded area the 95% confidence
interval of the mean. (D,H,L,andP)Similarbutforflag words related to rationality, science, and quantification: science,technology,scientific,chemistry,
chemicals,physics,medicine,model,method,fact,data,math,analysis,conclusion,limit,result,determine,transmission,assuming,system,size,unit,pres-
sure,area,percent (for Spanish: ciencia,tecnolog
ıa,cient
ıfico,qu
ımica,productos,f
ısica,medicina,modelo,m
etodo,dato,datos,hip
otesis,estad
ısticas,
c
alculo,an
alisis,conclusi
on,l
ımite,resultado,determinar,transmisi
on,sistema,tama~
no,unidad,presi
on,
area,densidad,porcentaje).
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Correlated Concepts
Although sentiment analysis allows a meaningful evaluation of
the affective components of language change, sentiment indica-
tors do not necessarily capture the full essence of the sea
change revealed by the PCA. This becomes evident if we exam-
ine the words correlating and anticorrelating most strongly to
the PCA axis, to the sentiment score, and to the overall hockey-
stick pattern. Lists of the 5% top-scoring words are given in SI
Appendix, section 10), but a glance at the 1% top-scoring words
for English already reveals that the patterns we find correspond
to a seesaw between two opposing poles of concepts (Table 1).
On the one hand we have words that may be broadly character-
ized as related to a personal view of the world (Table 1, top
row). At the contrasting end there are words that could be
characterized at first glance as related to societal systems (Table
1, bottom row). The idea that there has been a shift in empha-
sis from the collective to the individual over the past decades is
supported by a pronounced trend toward the use of singular
versus plural pronouns starting in the 1980s (Fig. 2).
A closer look at the short and also the longer (SI Appendix)lists
of words suggests that both the personal and the societal poles
encompass several distinct groups of concepts. On the personal
pole we have words that we could classify as related to belief, spiri-
tuality, sapience, and intuition (e.g., imagine,compassion,forgive-
ness,heal), senses (e.g., feel,smell,silence), and the body (e.g.,
knee,face,chest) but also personal pronouns (e.g., me,you,she)
and activities (e.g., walk,sleep,smile). By contrast, at the opposing
pole tentatively labeled as societal we have words related to sci-
ence and technology (e.g., experiment,circuit,chemistry,dean,
gravity), quantification (e.g., weigh,depth,greater,per), business and
economy (e.g., corporation,commissioner,salary,cost,shipping,
contract), social organization (e.g., jurisdiction,congress,minister,
department,commission,institution), and time and place (e.g., year,
month,january,district,west).
To test if those tentatively discerned concepts do indeed con-
tribute individually to the seesaw that we see in the PCA and
sentiment, we examined the historical dynamics of the separate
concept groups. Most of the groups are straightforward to
delineate. To obtain suggestions for populating the belief, spiri-
tuality, and intuition cluster we also used a thesaurus algorithm
available at relatedwords.org (combining search techniques
such as word embedding and Concept-Net). Examining dynam-
ics of each of the resulting clusters reveals a striking synchrony,
confirming that within each language the shifts in interest
across the concepts we identified happen very much in concert
(SI Appendix, section 4).
Arguably, the opposing poles of human versus societal con-
cepts we identified may also be interpreted in terms of how
they relate to two fundamentally different cognitive modes of
operation (10–13), namely system I (“thinking fast,” loosely
intuition) vs. system II (“thinking slow,” loosely rationality). We
test this idea by exploring clusters of words that we now filter
to specifically reflect those opposed modes of thinking (for
details see SI Appendix, section 4). Selected intuition flag words
are rooted in the concepts of belief, spirituality, sapience, intui-
tion, and senses, while the rationality flag words we used are
rooted in the concepts of science, technology, and quantifica-
tion (see SI Appendix, section 4 for a full account). Plotting the
dynamics of the frequencies of words in those clusters supports
the view that the PCA and sentiment patterns we revealed are
Table 1. Contrasting classes of concepts related to a personal (top row) vs. societal view of the world (bottom row) emerge by
ranking words according to their correlation with principal components, overall sentiment, and the hockeystick pattern
Words scoring highest on surging PCA axis
(PC2):
angry, look, walk, unexpected, sleep,
voice, imagine, embarrassed, tortured,
heal, struggling, knowing, potion,
ambush, incredible, looking, greedy,
terrified, looks, how, torture, learn, anger,
invisible, mother, comfortable, drunk,
fade, like, brutal, harsh, yourself, pain,
sofa, could, dream, distracted, crying,
what, thanks, her, eat, walking, shower,
helmet, warn, suspected, sense, luckily,
smell
Words correlating most positively to
sentiment:
dressed, nights, beating, mad, forget,
perfume, wore, delicious, crowd, dinner,
took, sister, whispering, saw, hung, next,
shut, bad, together, suddenly, slept,
beside, thought, away, stood, another,
awake, spoke, alive, drank, me, down,
broke, dark, blame, inviting, whisper,
drown, too, polite, moment, dragged, life,
hang, quietly, forgot, glow, silence,
footsteps, surprised
Words declining before 1980 and rising after
1980:
perfect, understood, throw, them,
embrace, sight, comfort, nothing, rushing,
place, trusting, awful, beautiful, ever,
hearts, never, awake, throwing, when,
sweet, promise, fallen, threw, cheer,
brother, so, spirit, breathe, every, owe,
believing, thankful, footsteps, him, rest,
stranger, gorgeous, seeing, supposed,
ashes, surprised, joy, cheering, disappoint,
stood, thrown, dare, who, shine, appetite
Words scoring lowest on surging PCA axis
(PC2):
secretary, state, report, year, sec, council,
order, authorized, district, west, eastern,
behalf, northern, president, office,
statement, under, January, vice, attorney,
east, committee, resident, October, south,
reference, officer, branch, annual, interest,
prepared, following, commonwealth,
August, counsel, exclusive, further, board,
April, collected, November, February, July,
jersey, September, jurisdiction, general,
contract, permanent, remaining
Words correlating most negatively to
sentiment:
deputy, separate, annual, surface, applied,
report, joint, contain, sub, marine, effect,
determined, counsel, established, foreign,
reasonable, congress, qualified, gross,
number, direct, violation, assigned, tables,
increase, request, section, savings,
remaining, temperature, library, permit,
construction, funds, reference, chemistry,
transportation, manual, provided, volume,
capital, chemical, assist, public, member,
retarded, demonstration, affected,
department, rate
Words rising before 1980 and declining after
1980:
area, program, indicate, available,
development, basis, determine, initial,
technical, million, addition, final, range,
replacement, personnel, control, unit,
involved, percent, eliminate, limited, rate,
concentration, increase, result, test, staff,
included, tested, transfer, maximum, zone,
plus, sample, recent, congressman, level,
funds, data, responsible, basic, laboratory,
equipment, budget, procedure,
breakdown, effective, activity, tape,
review
Listed are the words that score highest vs. lowest on the second PCA axis depicted in Fig. 1, the words that correlate most positively vs. negatively with
positive sentiment, and the words that increased most clearly after 1980 while declining between 1850 and 1980 vs. words that show the opposite pattern
(ranked to the absolute difference in Kendall tau in those periods). We used positive sentiment for computing the correlations in the second column, but
this is closely correlated to negative sentiment and arousal. Longer lists (5%) of English, and the analogous analysis of Spanish words, English fiction, and
English excluding fiction are presented in SI Appendix, section 10.
PSYCHOLOGICAL AND
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Scheffer et al.
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correlated to a systematic change along the rationality–intuition
gradient (Figs. 1 and 3). Moreover, looking at a small set of
intuition and rationality flag words across a larger collection
of languages (American English, British English, German,
French, Italian, and Russian) we find roughly similar patterns
(SI Appendix, section 9).
Fiction vs. Nonfiction
The Google n-gram corpus has an English Fiction category,
which is a subset from the overall English corpus, thus making
it possible to estimate word frequencies in the English subset
excluding fiction too (see SI Appendix, section 6). While the
resulting corpus (English excluding Fiction) cannot be consid-
ered free of fiction, it may safely be assumed that its relative
proportion of nonfiction is substantially higher than that in the
fiction corpus. Analysis of those subsets reveals that the propor-
tion of fiction in the n-gram database rose from about 5% up
till 1975 to about 35% in recent years (see SI Appendix, Fig.
S9). To explore how this affects the word balance in the overall
corpus we analyzed the patterns separately for English fiction
and nonfiction (Figs. 1–3 and SI Appendix, section 6). It turns
out that the dynamics of intuition flag words and rationality-
related words run in close parallel in fiction and nonfiction.
The magnitude of the surge in sentiment- and intuition-related
words is stronger in the overall English corpus than in the non-
fiction corpus (Fig. 1 Band Cvs. Nand O). This is likely an
effect of rising proportion of fiction in the database over the past
decades, as fiction is more biased towards intuition-related words
(compare the rationality–intuition ratio in Fig. 3 Dand E). More
generally, this result illustrates how changes in the representation
of contrasting genres in the Google n-gram database can affect
1.0
2.0
3.0
4.0 English All
A
'i'/ 'we'
'my' / 'our'
('she'+ 'he') / 'they'
('her' + 'his') / 'their')
1.5
3.0
4.5
6.0 Spanish
B
'yo' / 'nosotros'
'mi' / 'nuestro'
('ella'+ 'él') / ('ellas' + 'ellos')
10.0
2.5
5.0
7.5
12.5 English Fiction
C
'i'/ 'we'
'my' / 'our'
('she'+ 'he') / 'they'
('her' + 'his') / 'their')
1850
1875
1900
1925
1950
1975
2000
2025
year
1.0
0.5
1.5
2.0
2.5 English excl Fiction
D
'i'/ 'we'
'my' / 'our'
('she'+ 'he') / 'they'
('her' + 'his') / 'their')
[frequency singular pronoun(s)] / [frequency plural pronoun(s)]
Fig. 2. (A–D) Ratio of the relative frequencies of singular to corresponding plural pronouns in various book corpora represented in the Google n-gram
database.
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relative frequencies of words that are overly abundant or rare in
particular genres. As Google’s policy for including books has
changed over the past decade we also analyzed the 2009 version
of the n-gram database. Patterns turn out to remain robust, even
though the relative proportion of genres in this older database
was different (see SI Appendix,section7). Taken together these
results suggest that changes in the relative interest in the dimen-
sions of language we probed tend to be reflected rather broadly
across genres.
Comparison with
New York Times
and Google
Search Queries
For comparison with the book analyses we retrieved the use of
the two groups of key words (the same as shown in Fig. 1, right-
hand columns) in the New York Times since 1850 (see SI
Appendix,section5). The fraction of articles in which any of those
words occurs tends to rise over time (SI Appendix,section5).
However, the balance between rational and intuition words
reflects the same overall trend we find for books (Fig. 3),
suggesting that neither the long-term pattern nor its recent rever-
sal are specific to the book corpus. When it comes to interpreta-
tion, an important question that remains is whether trends in
word frequencies do to some extent reflect trends in public inter-
est in the corresponding concepts. This is difficult to probe in a
quantitative way. Nonetheless, since 2004 one proxy for interest
is the frequency with which people search a word using Google.
We therefore compared the trends in book words from 2004 till
2019 with trends of the same words in the same time period in
Google search queries (from https://trends.google.com;seeSI
Appendix,section8). It turns out that change in interest for par-
ticular words in Google search queries is more often positively
(and less often negatively) correlated to trends in use of the same
words in books than expected by chance (Fig. 4). This suggests
that indeed trends in book language do in part reflect trends in
interest.
Robustness and Potential Biases
Despite the close relationship between book trends and Google
search interests in recent years, it remains possible that the
long-term patterns we find are in part artifacts of the data and
our choice of words. With respect to the latter, the 5,000 most
frequent words in any language represent an overwhelming
sample of common language use, buffering against the problem
that any individual word may be subject to fashions or change
meaning. Our findings may, however, be subject to other sys-
tematic biases. For instance, our list of 5,000 most common
words and the emotional ranking of words was determined in
recent years and therefore reflects relatively recent language
use. Still, probably the most important caveat of using book
texts is that they are a biased representation of language, a bias
that may change over time (14, 15). What ends up in the uni-
versity libraries used for the Google n-gram data varies with
trends in Google’s book-inclusion policy, editorial practices,
library policies, and popularity of genres. As none of those
effects can be excluded it is important that we find the same
trends for word use in the New York Times. Also, the observa-
tion that rationality- and intuition-related words have the same
trends in fiction and in the general corpus minus assigned fic-
tion suggests that underlying shifts in attitude toward those
opposite poles of thinking tend to be reflected across genres,
an assertion consistent with the finding that legislative texts
have also become more informal over the past decades (16).
It is also worth noting that the link between book language
and social sentiment has been validated in other studies (17) and
that the long-term trend we find until 1980 is in line with what
has been found in other studies including different text corpora
and different indicators. For instance, a study comparing a corpus
0.4
0.6
0.8
1.0
1.2 NYT
A
0.6
1.2
1.8
2.4
3.0
English All
B
0.2
0.4
0.6
0.8
1.0 Spanish
C
0.18
0.24
0.30
0.36 English Fiction
D
1850
1875
1900
1925
1950
1975
2000
2025
year
0.6
1.2
1.8
2.4
3.0 English excl Fiction
E
[average frequency Rationality words] / [average frequency Intuition words]
Fig. 3. Ratio of intuition to rationality related words in the New York
Times (A) and various book corpora represented in the Google n-gram
database (B–E). The graphs depict the ratio of the mean relative frequen-
cies of the sets of rationality-related and intuition-related flag words
presented in Fig. 1, right-hand columns.
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of New York Times articles from 1851 to 2015 and the Google
Books corpus from 1800 to 2000 showed (18) that in both cor-
pora there has been a significant downward trend in positive as
well as negative words as classified through the Linguistic Inquiry
and Word Count (LIWS) system (19). Another study, using a
corpus from the New York Times, a corpus of scientific articles,
and a corpus of Google Books, revealed that over the past two
centuries in all those corpora there has been a significant increase
of words reflecting causal reasoning as reflected by words in the
“cause” category in the LIWS system (a list of 108 words such as
because,since,hence,how,why,depends,andimplies)(20).
In conclusion, we cannot exclude the possibility that our
results are biased by as-yet-unknown effects in the data. For
instance, the rise of more casual language in the Google Books
data could be related to the digital transformation enabling
libraries to collect a larger diversity of materials while staying
within budget. Such “known unknowns” as well as yet “unknown
unknowns” should be subject to future research. Nonetheless,
studies using different corpora as well as different marker words
confirm the long-term decline of sentiment-laden (positive and
negative) language and a rise of words related to causal reason-
ing. Meanwhile, the dramatic recent reversal of this trend occurs
in fiction as well as a corpus from which much fiction has been
filteredandisalsofoundinourNew York Times analysis. Thus,
while it will be important to explore alternative word collections,
sentiment classifications, and text corpora it seems likely that the
marked U-shaped pattern we find reflects a true dimension of
language change.
Potential Drivers
Inferring the drivers of this stark pattern necessarily remains
speculative, as language is affected by many overlapping social
and cultural changes. Nonetheless, it is tempting to reflect on a
few potential mechanisms. One possibility when it comes to the
trends from 1850 to 1980 is that the rapid developments in sci-
ence and technology and their socioeconomic benefits drove a
rise in status of the scientific approach, which gradually perme-
ated culture, society, and its institutions ranging from the edu-
cation to politics. As argued early on by Max Weber, this may
have led to a process of “disenchantment” as the role of spiritu-
alism dwindled in modernized, bureaucratic, and secularized
societies (21, 22).
What precisely caused the observed stagnation in the long-
term trend around 1980 remains perhaps even more difficult to
pinpoint. The late 1980s witnessed the start of the internet and
its growing role in society. Perhaps more importantly, there
could be a connection to tensions arising from neoliberal poli-
cies which were defended on rational arguments, while the eco-
nomic fruits were reaped by an increasingly small fraction of
societies (23–25).
In many languages the trends in sentiment- and intuition-
related words accelerate around 2007 (SI Appendix, section 9).
One possible explanation could be that the standards for inclu-
sion in Google Books shifted from “being in a library Google
had an agreement with” to “from a publisher that directly
deposited with Google” after 2004 to 2007, thus affecting the
corpus composition. The 2007 shift also coincides with the
global financial crisis which may have had an impact. However,
earlier economic crises such as the Great Depression (1929 to
1939) did not leave discernable marks on our indicators of
book language. Perhaps significantly, 2007 was also roughly the
start of a near-universal global surge of social media. This may
be illustrated by plotting the dynamics of the word “Facebook”
as a marker alongside the frequency of a set of intuition and
rationality flag words in different languages (SI Appendix,
section 9).
−60
−40
−20
0
20
40
60
80 English All
A
−100
−50
0
50
100 Spanish
B
−40
−20
0
20
40
English Fiction
C
−1.00
−0.75
−0.50
−0.25
0.00
0.25
0.50
0.75
1.00
Spearmans rank correlation coefficient
−40
−20
0
20
40
60 English excl Fiction
D
[frequency pairwise words] - [average frequency null model]
Fig. 4. (A–D) Relationship between trends in the use of words in Google
search queries and use of the same words in books for the period 2004 to
2019. Blue bars represent frequency distributions of Spearman rank corre-
lations between each word in books and that same word in Google
searches after subtracting average frequencies predicted by a null model
of randomly matched words (50 bins). To construct the null model we
matched the 5,000 words in books with randomly picked words from the
same word list in Google searches and calculated the Spearman rank cor-
relation. We ran this 1,000 times, resulting in a frequency distribution for
each correlation bin. We subtracted the mean from the resulting distribu-
tion, such that the null line represents the average frequency of correla-
tions (for more details see SI Appendix). Black lines represent the 5% and
95% percentiles of the frequency distributions of the null model per corre-
lation bin. For each of the corpora, positive correlations are found more
than expected by chance, while negative correlations are found less than
expected.
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Various lines of evidence underpin the plausibility of an
impact of social media on emotions, interests, and worldviews.
For instance, there may be negative effects of the use of social
media on subjective well-being (26). This can in part be related
to distortions such as the perception that your friends are more
successful, have more friends, and are happier (27, 28) and
more beautiful (29) than you are. At the same time, a percep-
tion that problems abound may have been fed by activist groups
seeking to muster support (30) and lifestyle movements seeking
to inspire alternative choices (31). For instance, social media
catalyzed the Arab Spring, among other things, by depicting
atrocities of the regime (32), jihadist videos motivate terrorists
by showing gruesome acts committed by US soldiers (33), and
veganism is promoted by social media campaigns highlighting
appalling animal welfare issues (31). Many of the problems
highlighted on social media will be real, and they may have
been hidden from the public eye in the past. However, indepen-
dently of whether problems are exaggerated or merely revealed
online, the popular effect of such awareness campaigns may be
the perception of an unfair world entangled in a multiplicity of
crises. Further down the gradient from revelation to exaggera-
tion we find misinformation. The spread of misinformation (34)
and conspiracy theories (35) may be amplified by social media,
as the online diffusion of false news is significantly broader,
faster, and deeper than that of true news and efforts to debunk
(36). Conspiracy theories originate particularly in times of
uncertainty and crisis (35, 37) and generally depict established
institutions as hiding the truth and sustaining an unfair situa-
tion (38). As a result, they may find fertile grounds on social
media platforms promulgating a sense of unfairness, subse-
quently feeding antisystem sentiments. Neither conspiracy theo-
ries nor the exaggerated visibility of the successful nor the
overexposure of societal problems are new phenomena. How-
ever, social media may have boosted societal arousal and senti-
ment, potentially stimulating an antisystem backlash, including
its perceived emphasis on rationality and institutions.
Importantly, the trend reversal we find has its origins decades
before the rise of social media, suggesting that while social media
may have been an amplifier other factors must have driven the
stagnation of the long-term rise of rationality around 1975 to
1980 and triggered its reversal. Perhaps a feeling that the world is
runinanunfairwaystartedtoemergeinthelate1970swhen
results of neoliberal policies became clear (23–25) and became
amplified with the rise of the internet and especially social media.
A central role of discontent would be consistent with the rise in
language characteristic of so-called cognitive distortions (39)
known in psychology as overly negative attitudes toward oneself,
the world, and the future (40–43). If disillusion with “the system”
is indeed the core driver, a loss of interest in the rationality that
helped build and defend the system could perhaps be collateral
damage.
Outlook
It seems unlikely that we will ever be able to accurately quantify
the role of different mechanisms driving language change. How-
ever, the universal and robust shift that we observe does suggest
a historical rearrangement of the balance between collectivism
and individualism and—inextricably linked—between the rational
and the emotional or framed otherwise. As the market for books,
the content of the New York Times, and Google search queries
must somehow reflect interest of the public, it seems plausible
that the change we find is indeed linked to a change in interest,
but does this indeed correspond to a profound change in atti-
tudes and thinking? Clearly, the surge of post-truth discourse
does suggest such a shift (44–48), and our results are consistent
with the interpretation that the post-truth phenomenon is linked
to a historical seesaw in the balance between our two fundamen-
tal modes of thinking. If true, it may well be impossible to reverse
the sea change we signal. Instead, societies may need to find a
new balance, explicitly recognizing the importance of intuition
and emotion, while at the same time making best use of the
much needed power of rationality and science to deal with topics
in their full complexity. Striking this balance right is urgent as
rational, fact-based approaches may well be essential for main-
taining functional democracies and addressing global challenges
such as global warming, poverty, and the loss of nature.
Data Availability. All codes and data are available at https://github.com/
jbollen/rise_and_fall_of_rationality_in_language.
ACKNOWLEDGMENTS. This work is supported by a Spinoza award granted to
M.S. by the Netherlands Organization for Scientific Research, and by the
NESSC Gravitation grant from the Dutch Ministry of Education, Culture and
Science (024.002.001). J.B. is grateful for support from the Urban Mental
Health Institute of the University of Amsterdam, Wageningen University and
Research, the NSF (NSF Social, Behavioral and Economic Sciences [SBE]
1636636), and the support of the Indiana University Vice Provost for COVID-19
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