Why are song lyrics becoming simpler? a time series analysis of lyrical complexity in six decades of American popular music

Abstract

Song lyrics are rich in meaning. In recent years, the lyrical content of popular songs has been used as an index of culture’s shifting norms, affect, and values. One particular, newly uncovered, trend is that lyrics of popular songs have become increasingly simple over time. Why might this be? Here, we test the idea that increasing lyrical simplicity is accompanied by a widening array of novel song choices. We do so by using six decades (1958–2016) of popular music in the United States (N = 14,661 songs), controlling for multiple well-studied ecological and cultural factors plausibly linked to shifts in lyrical simplicity (e.g., resource availability, pathogen prevalence, rising individualism). In years when more novel song choices were produced, the average lyrical simplicity of the songs entering U.S. billboard charts was greater. This cross-temporal relationship was robust when controlling for a range of cultural and ecological factors and employing multiverse analyses to control for potentially confounding influence of temporal autocorrelation. Finally, simpler songs entering the charts were more successful, reaching higher chart positions, especially in years when more novel songs were produced. The present results suggest that cultural transmission depends on the amount of novel choices in the information landscape.

Full text

RESEARCH ARTICLE
Why are song lyrics becoming simpler? a time
series analysis of lyrical complexity in six
decades of American popular music
Michael E. W. VarnumIDs
1
*, Jaimie Arona Krems
2
*, Colin Morris
3
*,
Alexandra WormleyID
1
, Igor Grossmann
4
*
1Department of Psychology, Arizona State University, Tempe, Arizona, United States of America,
2Department of Psychology, Oklahoma State University, Stillwater, OK, United States of America,
3Toronto, Canada, 4Department of Psychology, University of Waterloo, Waterloo, ON, Canada
*mvarnum@asu.edu (MEWV); jaimie.krems@okstate.edu (JAK); colin.morris2@gmail.com (CM);
igrossma@uwaterloo.ca (IG)
Abstract
Song lyrics are rich in meaning. In recent years, the lyrical content of popular songs has
been used as an index of culture’s shifting norms, affect, and values. One particular, newly
uncovered, trend is that lyrics of popular songs have become increasingly simple over time.
Why might this be? Here, we test the idea that increasing lyrical simplicity is accompanied
by a widening array of novel song choices. We do so by using six decades (1958–2016) of
popular music in the United States (N= 14,661 songs), controlling for multiple well-studied
ecological and cultural factors plausibly linked to shifts in lyrical simplicity (e.g., resource
availability, pathogen prevalence, rising individualism). In years when more novel song
choices were produced, the average lyrical simplicity of the songs entering U.S. billboard
charts was greater. This cross-temporal relationship was robust when controlling for a range
of cultural and ecological factors and employing multiverse analyses to control for potentially
confounding influence of temporal autocorrelation. Finally, simpler songs entering the charts
were more successful, reaching higher chart positions, especially in years when more novel
songs were produced. The present results suggest that cultural transmission depends on
the amount of novel choices in the information landscape.
Introduction
Music is a human universal [1,2], and it is known to influence cognition, affect, and behavior
[3–5]. Because songs—and particularly popular song lyrics—can be so rich in meaning [6,7],
social scientists have long explored the ways that such lyrics intersect with some fundamental
social processes, including identity formation and person perception [8–13].
More recently, social psychologists have begun to view music as a cultural product and to
examine the ways that popular music lyrics reflect important aspects of psychology at the cul-
tural level; the content in popular lyrics indexes changing norms, affect, and/or values [5,14–
19]. For example, DeWall and colleagues explored popular song lyrics as a “window into
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OPEN ACCESS
Citation: Varnum MEW, Krems JA, Morris C,
Wormley A, Grossmann I (2021) Why are song
lyrics becoming simpler? a time series analysis of
lyrical complexity in six decades of American
popular music. PLoS ONE 16(1): e0244576.
https://doi.org/10.1371/journal.pone.0244576
Editor: Ronald Fischer, Victoria Univ Wellington,
NEW ZEALAND
Received: July 3, 2020
Accepted: December 11, 2020
Published: January 13, 2021
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0244576
Copyright: ©2021 Varnum et al. 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 and
reproducible code for analyses reported in the
manuscript are available on the Open Science
Framework (https://osf.io/qnsmj/).

understanding U.S. cultural changes in psychological states” [5, pp. 200], finding that popular
songs lyrics from 1980–2007 reflected an increase in self-focus and a decrease in other-focus.
Here, we demonstrate that popular music lyrics have become increasingly simple over time,
and we test one possible explanation for this surprising trend, namely that the amount of novel
song choices has increased.
Novel song choices and lyrical simplicity
Several lines of evidence suggest that people may have baseline preferences for songs with sim-
pler lyrics. One of the most widely known phenomena in psychology is the mere exposure
effect, a phenomenon where repeated exposure to a non-aversive stimulus increases preference
for it [20–22]. One implication of this principle for the present question is that simpler, more
repetitive lyrics as these pieces essentially have this effect baked into them and thus may tend
to be preferred all other things being equal. Further, songs with more repetitive lyrics may
enjoy certain advantages in terms of information transmission as they are easier to remember
[23] and likely easier to transmit with fidelity [24–26]. Further, recent work has shown that
naïve listeners find simpler, more repetitive pieces of music to be more enjoyable, engaging,
and memorable [27,23].
Why might pop songs become lyrically simpler in times when more new songs are pro-
duced? Theory and research from diverse literatures suggest that songs with simpler lyrics
might be especially successful when there are more new songs to choose from. First, humans
are cognitive misers. People have limited information-processing capacities [28], and are
known to conserve mental resources [29]. Consequently, humans often use shortcuts in deci-
sion-making [30,31]. For example, when confronted with the task of evaluating persuasive
messages and/or complex decision environments, people are more likely to use heuristics,
peripheral cues, and other automatic cognitive processes to evaluate these messages if cognitive
resources are limited in some fashion [32,33]. Thus, when there are more products to be eval-
uated, people may increasingly prefer simpler products as they may require less mental effort
to engage with. The mere exposure effect might also have a greater influence on decision mak-
ing in such contexts as well, given that it too can be thought of as a heuristic or even instinctive
evaluation. Further, across real-world studies and in-laboratory experiments, when people are
confronted with a greater number of options to choose from, they are more likely to choose
simpler, less cognitively demanding products [34]. Taken together, this work suggests that pop
songs on average might become lyrical simpler in times when people are exposed to greater
amounts of new songs and that success of such songs might be more strongly linked to lyrical
simplicity in such times.
Here, we test the hypothesis that the trend toward increasingly simple popular music lyrics
might be accompanied by the increasing number of songs released each year, using six
decades’ worth of song data. We also do so while including a number cultural and ecological
control variables, as prior work demonstrates that well-studied ecological features, such as
resource levels, pathogen threat, and sources of external threat (e.g., climatic stress, armed con-
flict) can impact markers of cognition and behavior at the cultural-level [35–38], and might
plausibly affect preferences for simplicity in aesthetic products. For example, both resource
scarcity and pathogen prevalence have been associated with conformity, innovation, and crea-
tivity in prior work [35,39,40].
Methods
We gathered cross-temporal data covering a period of six decades (1958–2016) on lyrical com-
pressibility (as an index of simplicity/complexity of song lyrics), amount of novel songs
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Funding: The author(s) received no specific
funding for this work.
Competing interests: The authors have declared
no competing interests exist.

produced (as an index of available novel song choices), and ecological, socioecological, and
cultural variables linked to patterns of cultural change in previous research or plausibly related
to trends in aesthetic content.
Lyrical compressibility of successful music
We gathered data from 14,661 songs that entered the Billboard Hot 100 charts spanning the
period from 1958 (the charts inception) to 2016. The Billboard Hot 100 tracks the 100 most
popular songs each week based on music sales, radio airplay, and internet streaming. To opera-
tionalize lyrical complexity (vs. simplicity), we estimated text compressibility. By operationaliz-
ing complexity via a compressibility index, we avoided some of the conceptual ambiguity
associated with operationalization of complexity in prior research [40–42]: Whereas multi-pur-
pose use of a single product may reflect product’s complexity from the operational standpoint,
it may also represent greater simplicity from the standpoint of consumer psychology. Further,
song lyrics are tractable to work with when using an automated compression algorithm.
Compressibility indexes the degree to which song’s lyrics have more repetitive and less
information dense, and thus simpler, content. We used a variant of the established LZ77 com-
pression algorithm. In brief, the LZ77 algorithm works by finding repeated substrings and
replacing them with ’match’ objects pointing back to the string’s previous occurrence. A
match is encoded as a tuple (D,L), with Dbeing the distance to the substring’s previous occur-
rence, and Lbeing its length. We treated these matches as costing 3 bytes. This way, a repeated
string only leads to space savings if it is of at least length 4, and longer repetitions lead to
greater relative savings. Given a song S, and the set of matches Mproduced by the LZ77 algo-
rithm when applied to that song, its compressed size is therefore:
compsizeðSÞ ¼ jSj  X
ðD;LÞ2M
L3
Where |S| is the original size of the song’s lyrics, measured in characters/bytes. The compres-
sion ratios of songs in our dataset (i.e., |S|/compsize(S)) followed an approximately log-normal
distribution, so we operationalized compressibility as the logarithm of this ratio:
compressibilityðSÞ ¼ logðjSj
compsizeðSÞÞ
We used the LZ77 compression algorithm because of its intimate connection to textual repeti-
tion. Most of the byte savings when compressing song lyrics arise from large, multi-line sec-
tions (most importantly the chorus, and chorus-like hooks). Another significant contributor
are multi-word phrases, which may be repeated in variations across different lines for poetic
effect (e.g. the anaphoric verses in Lady Gaga’s Bad Romance: "I want your ugly / I want your
disease / I want your everything . . ."). The compression may make use of repeated individual
words, or even sub-word units that repeat (perhaps incidentally), but their contribution to the
overall compressibility is low.
Higher compression scores signify more repetition and therefore higher simplicity. A score
of 0 means no compression was possible (e.g. if the input were random noise), a score of 1
means a 50% reduction in size, a score of 2 means a 75% reduction, and so on. For example,
Daft Punk’s 1997 song “Around the World” repeats the title 144 times and has a compressibil-
ity score of 5.42 (the maximum in this sample). Nat King Cole’s “The Christmas Song” (1961)
has a low compression score of 0.11.
We computed mean compressibility for each year based on all songs that entered the Hot
100 charts in a given year for which we were able to scrape lyrics (1958–2016). Because we
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used an automated procedure for song scraping, which depends on the readability of the song
lyrics, the percentage of songs scraped varied between 27% of top 100 songs in 1958 and 91%
of songs in 2015 (M= 57%, Md = 57%, SD = 19%). Because percentage of scraped songs has
been increasing over time, and correlated with the compressibility index, τ= .73, p<.001, in
additional analyses we controlled for this trend.
Song success
Some of the theoretical positions we draw on to evaluate possible reasons for changes in lyrical
complexity suggest that more compressible songs may be more likely to be successful. To eval-
uate this proposition, we additionally gathered data on the highest position of each song in the
sample achieved on the Billboard charts.
Novel music production
In the spirit of the multiverse analyses [43], we used three separate indicators to assess the
amount of new music to which people are likely exposed in a given year. For each year (1958–
2016) we computed the total number of songs which made the Hot100 chart, the number of
musical releases per year according to Discogs (Discogs.com), and the number of Wikipedia
entries about songs first published or performed each year (Wikipedia.org).
Possible ecological drivers of cultural change in aesthetic preferences and
music production
We assessed a range of well-studied socioecological factors (e.g., resource levels, pathogen
threat, sources of external threat), which could plausibly bear on aesthetic preferences or
might affect lyrical simplicity (and whether the predicted association between novel music pro-
duction and simplicity holds even controlling for these or other ecological and cultural vari-
ables discussed below). Resource scarcity has been linked to greater conformity [39] and cross-
temporal work has found that greater resource levels are linked to more innovation and crea-
tive output [40] and less conformity [44,45]. Higher levels of infectious disease have also been
linked to more conformity [46,47], traditionalism [48], and tight social norms [35,49]. Exter-
nal threats, due to climate or war, have also been linked to more traditional outlooks and tight
social norms [49], which might similarly bear on trends in lyrical simplicity. We thus included
publicly accessible data indexing these factors GDP per capita, GDP growth, unemployment,
pathogen prevalence, climatic stress, and participation of the US in major armed conflicts. The
data used in our analyses covered the years 1958–2016. Data on GDP per capita and GDP
growth were gathered from macrotrends.net, and data on the other markers came from Var-
num & Grossmann [50] and updates from the original data sources used in that publication.
We also explore the possible impact of other socioecological factors that might plausibly
affect lyrical simplicity. One might speculate that immigration could drive increases in lyrical
simplicity. For example, simpler lyrics in American pop songs might be linked to shifts in the
amount of people for whom English may not be a first language. In a similar way, it might be
that ethnic fractionalization, so far linked to changes in individualism and uniqueness over
time [51]¸ may also increase preferences for, memory of, and/or dispersal of simpler, more
repetitive lyrics, as such content would be easier to convey and understand to a wide range of
audiences. To assess the possibility that a rise in simpler English lyrics might be linked to shifts
in the amount of people for whom English may not be a first language, we used data on the
number of green cards issued from the Department of Homeland Security as a marker of
immigration. To assess possibilities linked to ethnic fractionalization, we used data on ethnic
fractionalization from the US Census Bureau.
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Research on the consequences of residential mobility also suggests that perhaps this variable
might also affect lyrical trends. Previous studies have linked residential mobility to greater sus-
ceptibility to the mere exposure effect and greater preference for familiar cultural products
[52]; thus, it may be that mobility is also linked to temporal variations in lyrical complexity of
pop songs. To assess residential mobility, we gathered data on percentage of the US population
that changed residence within the US from the US Census Bureau.
At the same time, a simpler variable might also be driving this effect. Perhaps products that
succeed with a larger audience are merely simpler, akin to a lowest common denominator
effect. Because the U.S. population grew substantially in recent decades, we also test whether
population trends might be associated with lyrical simplicity. Thus, we also gathered data on
the total size of the US population from macrotrends.net to explore population size.
Cultural factors
Prior work has found conservatives show a preference for simple and unambiguous art, speech
patterns, and literature [53–57] (though see also Conway et al., 2016 [58]). Thus, one might
suspect that possible changes in conservatism could be driving lyrical simplicity. Somewhat
similarly, other evidence suggests that cross-cultural differences in aesthetic preferences and
expression are linked to orientations toward collectivism [59,60]. Thus, we also gathered data
on indicators of conservative ideology, operationalized conservatism as the average percent of
annual survey respondents in Gallup polls identifying as conservative, and we included as an
index of cultural level collectivism based on frequency of collectivism related words in the
Google Ngrams American English corpus [45].
Analytic procedure
Where possible, we use non-parametric ordinal-level measures of correlation or partial corre-
lation (Kendall’s rank correlation coefficient τ), which provides estimate of similarity of the
orderings of the data when ranked by each of the quantities. Since Fechner’s initial work on
time series analyses, Kendall’s τhas been a preferred metric for examining cross-temporal rela-
tionships [61]. It provides a conservative estimate, which is preferred because time series data
is rarely normally distributed. Results were comparable when we used Pearson’s ror partial
Pearson correlations. In the initial step, we examined zero-order relationships between each of
the three indices of available novel song choices and average lyrical compressibility of popular
songs. Next, we created a composite index of novel song choices and assessed the robustness of
the hypothesized link between amount of novel song choices and average lyrical compressibil-
ity of popular songs by controlling for a host of ecological, socioecological, and cultural factors
that might plausibly influence cultural level success for simplicity vs. complexity. Our chief
analyses focused on a set of corrective analyses, in which we controlled for the possibly spuri-
ous nature of the relationship between our key time series due to temporal autocorrelation.
Given the range of possibilities of correcting for temporal autocorrelation, we opted to per-
form three different types of analyses that correct or account for the possibility that observed
relationships might be spurious as a function of autocorrelation in the time series. First, we
computed adjusted significance thresholds based on the Tiokhin-Hruschka procedure [62].
Second, we detrended our novel song production and lyrical compressibility time series by
residualizing for year and assessed the correlation between our detrended variables. Finally,
for central univariate and multivariate analyses, we used an automated auto-regressive inte-
grated moving average forecasting model (auto.ARIMA) to assess the relationship between
novel song choices and lyrical compressibility [63]. This technique involves a machine learning
algorithm that tests a number of different possible models which vary in autoregressive
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components, differencing, and moving average components, as well as whether they include
an exogenous predictor. Additionally, we used auto.ARIMA to generate a forecast for future
patterns of lyrical compressibility (2017–2046).
For multivariate analyses we entered multiple predictors of lyrical compressibility over
time. To avoid multicollinearity and overfitting (and due to limited number of units at the
yearly level of analysis), we first aggregated covariance scores attributed to additional socioeco-
logical and cultural factors (see Table 1) by performing a principal component analysis on
these covariates and saving component scores for further multivariate time series analyses.
The first principal component explained 50% of the variance in the covariates, with strong
loadings (absolute value >.85) for Population Size, GDP/capita, Residential Mobility, Patho-
gen Prevalence, Ethnic Heterogeneity and Immigration, moderate loadings for Armed Con-
flicts (.49) and weak loading of GDP growth (.44). Other covariates (Climatic Stress,
Unemployment, Conservatism, Collectivism) showed very weak loadings (.21 <absolute
value .27). Next, we entered both yearly music production scores and covariate-PCA scores
as independent predictors of lyrical compressibility, simultaneously accounting for the time
series structure in the data.
Data availability
All data and reproducible code for analyses reported in the manuscript are available on the
Open Science Framework (https://osf.io/qnsmj/).
Table 1. Correlations with average lyrical compressibility.
Variable Kendall’s τ Kendall’s τ(Detrended)
INFORMATION
LANDSCAPE
Music Production .714 .222
ECOLOGICAL
GDP per capita .733 .044
GDP growth -.260 -.073
Unemployment .051 .135
Pathogen Prevalence -.490 .324
Climatic Stress -.118 .050
Armed Conflict .229.063
SOCIO-
ECOLOGICAL
Immigration .563 -.155
Ethnic Heterogeneity .737 -.066
Residential Mobility -.692 -.230
Population Size .726 .135
CULTURAL
Conservatism -.019 -.287
Collectivism -.225-.124
p<.05,
 p.01,
 p.001.
https://doi.org/10.1371/journal.pone.0244576.t001
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Results
Indicators of novel song choices and average lyrical compressibility
As Fig 1 indicates, mean lyrical compressibility (i.e., simplicity) of songs increased over time,
Kendall’s τ= .726, p<.001, as did number of songs making the Hot 100 charts per year, Ken-
dall’s τ= .425, p<.001, number of music releases according to Discogs per year, Kendall’s τ=
.973, p<.001, and number of Wikipedia entries for songs by year of publication, Kendall’s τ=
.871, p<.001.
Analyses of the composite index of novel song choices
Hot100 songs, Discog music releases, and Wikipedia song entries were highly correlated,
.41 <Kendall’s τ’s .87, and formed a single principle component with highest loadings by
the Wikipedia song entries (.98), and weakest loading by the Hot 100 songs (.88). To avoid
multicollinearity, we used component scores for further analyses. Overall, this index of
novel music production was strongly positively related to compressibility, Kendall’s τ=
.714¸ p<.001. Consistent with our predictions, mean lyrical compressibility per year was
positively correlated with amount of novel music produced per year as operationalized by
three distinct indicators, Kendall’s τ(nsongs in Hot 100 charts/year) = .429, p<.001, Ken-
dall’s τ(n Discogs music releases / year) = .721, p<.001, Kendall’s τ(nWikipedia entries
about songs/year) = .680, p<.001.
Fig 1. Change in lyrical compressibility, along with a music production-based forecast for future lyrical compressibility from
regression with ARMIA (1,0,0) and index of novel song choices as an exogenous predictor. Light purple indicates 95% confidence
bands, dark purple indicates 80% confidence bands.
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Relationships between socioecological factors and compressibility
Although several ecological dimensions were associated with changes in average lyrical com-
pressibility over time (see Table 1), these relationships were often in the opposite direction that
prior research or theorizing would suggest. For example, there were significant negative corre-
lations between GDP per capita and pathogen prevalence and average lyrical compressibility.
Further, our two cultural variables were either unrelated to lyrical compressibility (conserva-
tism) or correlated in the opposite of the predicted direction (collectivism). We did observe
theoretically sensible relationships between compressibility and residential mobility, immigra-
tion, ethnic fractionalization, and population size. However, when controlling for the poten-
tially confounding effect of temporal auto-correlation by residualizing out the effect of year,
only three of these relationships are statistically significant, and only the relationship between
pathogen prevalence and average lyrical complexity remains in a theoretically sensible direc-
tion (see Table 1).
Robustness analyses: Control variables
This PCA-based composite index of music production remained significantly related to lyrical
compressibility when including percentage of scraped songs/year as a covariate, Kendall’s τ
p
=
.261¸ p= .003. Further, it remained significant when controlling separately for each of the 12
specified control variables, .220 <partial Kendall’s τ’s <.770, p’s <.02 (see Table 2 for details).
Full correlations between these variables are presented in S1 Fig.
Robustness analyses: Auto-correlation
Importantly, the correlation between this composite index of novel song choices and average
lyrical compressibility remained significant when adjusting significance thresholds using the
Tiokhin-Hruschka method to account for observed auto-correlation in the two time series, r=
Table 2. Partial correlations between novel music production index and average lyrical compressibility.
Control Variable Partial Kendall’s τNovel Music Production & Lyrical Compressibility
ECOLOGICAL
GDP per capita .248
GDP growth .695
Unemployment .753
Pathogen Prevalence .596
Climatic Stress .710
Armed Conflict .696
SOCIO-
ECOLOGICAL
Immigration .539
Ethnic Heterogeneity .267
Residential Mobility .436
Population Size .231
CULTURAL
Conservatism .670
Collectivism .610
p<.05,
 p.01,
 p.001.
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.877¸
corrected
p<.001. As an alternative method for dealing with autocorrelation, we also
detrended the time series by residualizing out the linear impact of year. The correlation for our
detrended variables remained significant, Kendall’s τ= .222, p= .010.
Given the time series nature of our data, another way to test the hypothesized link between
amount of new songs available and average compressibility of these songs while also address-
ing the issue of autocorrelation can involve an automated ARIMA algorithm (auto.ARIMA)
within the forecast package [64] in R4.0.0 [65]. This machine-learning algorithm inspects the
time-series data to fit the optimal forecasting function. The auto-regressive (AR(p)) compo-
nent refers to the use of past values in the regression equation for the series Y. The auto-regres-
sive parameter p specifies the number of lags used in the model. A moving average (MA(q))
component represents the error of the model as a combination of previous error terms e
t
. The
order qdetermines the number of terms to include in the model. ARIMA models are well-
suited for long-term time series, such as the historic patterns in the present data. The auto-
mated algorithm within the forecast package searches through combinations of order parame-
ters and picks the set that optimizes model fit criteria, comparing Akaike information criteria
(AIC) or Bayesian information criteria (BIC) of respective models. Notably, the automated
forecasting approach allows us to specify an exogenous predictor such as novel song choices,
such that the automated function can evaluate the extent to which this exogenous predictor
improves the fit above and beyond the decomposition of the time-series of the dependent vari-
able. In other words, the automated function provides a conservative way to see whether an
exogenous predictor such as the novel song choices index improves accuracy in forecasts of
the lyrical compressibility. If the final model selected by auto.ARIMA includes our putative
exogenous variable (in this case amount of novel song choices), then this suggests that this var-
iable helps the model to achieve optimal fit to the data.
The results of this automated forecasting procedure indicated that a model with a positive
autoregressive component, B= .527, SE = .124, and a positive contribution of the novel music
production index, B= .059, SE = .008, provides the best fit to the data:
ytðlyrical compressibility functionÞ ¼ :983 þ:527yt1þ:059xþet
This model estimation suggests that the index of novel song choices contributes to average lyri-
cal compressibility above and beyond the temporal autocorrelation observed for average lyrical
compressibility. Further, the coefficient for the index of novel song choices was statistically sig-
nificant, z= 6.95, p<.001.
We also ran an alternative set of auto.ARIMA analyses where we set novel song choices as
the dependent variable and average lyrical compressibility as an exogenous predictor. The
results of this automated forecasting procedure indicated that a model with two positive mov-
ing average components, B= 1.176, SE = .242, and B= .487, SE = .164, and a positive contribu-
tion of average lyrical compressibility, B= 5.067, SE = 2.207, provides the best fit to the data:
ytðnovel music production functionÞ ¼  4:991 þ1:176εt1þ0:487εt2þ5:067xþet
The coefficient for lyrical compressibility was statistically significant, z = 2.30, p= .02.
Comparison of the Akaike Information Criterion (AIC) and Bayesian Information Crite-
rion (BIC) values for our primary and alternative models suggest that our primary model with
novel song choices as an exogenous predictor and lyrical compressibility as the dependent var-
iable, AIC = -235.84, BIC = -227.53, is superior to the alternate model with lyrical compress-
ibility as an exogenous predictor and novel song choices as the dependent variable,
AIC = 58.36, BIC = 68.75.
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Robustness analyses: Controlling for percentage of scraped songs
Because of a positive association between lyrical compressibility and percentage of scraped
songs per year, we performed a separate set of analyses in which we first regressed out the
effect of sampling (% of scraped songs/year) on lyrical compressibility and performed an auto.
ARIMA analysis on the residuals. Results of a model on the residuals with music production as
a predictor indicated a significant effect of music production, B= .799, SE = 0.046, z= 17.32, p
<.001, suggesting that the effect songs even when accounting for the possible change in
sampling.
Multivariate analyses
In another set of control analyses, we performed an auto.ARIMA analysis, in which we
included the PCA factor formed by all socio-ecological covariates as a second covariates. By
comparing the magnitude of the effect from this first principal component (which was chiefly
driven by ecological variables) and music production index, we can assess the relative contri-
bution of the music production index via-a-vis other socio-ecological covariates. The results of
this automated forecasting procedure indicated that a model with a positive autoregressive
component, B= .513, SE = .118, a significant positive contribution of the novel music produc-
tion index, B= .038, SE = .016, z= 2.37, p= .018, and a non-significant positive trend formed
by ecological covariates (and chiefly reflecting economic and population growth), B= .026, SE
= .016, z= 1.61, p= .108, provides the best fit to the data:
yt¼:981 þ:513yt1þ:038ðmusic productionÞ þ :026ðecological covariatesÞ þ et
This model estimation suggests that the index of novel song choices contributes to average lyri-
cal compressibility above and beyond the temporal autocorrelation as well as other ecological
covariates observed for average lyrical compressibility. Moreover, the effect of music produc-
tion on lyrical compressibility was stronger than other feasible covariates explored in the pres-
ent dataset.
Exploratory song-level analyses
In exploratory analyses we evaluated how lyrical compressibility is associated with song suc-
cess, and whether this relationship was stronger in time periods when more novel music was
produced. Given that we shifted focus to song-specific data, we utilized a multi-level frame-
work via lme4 package in R, with songs’ chart position and lyrical compressibility scores nested
within years. Preliminary auto.ARIMA analyses on the yearly aggregate data indicated that a
model with no auto-regressive components but a linear trend would show the best model fit.
Therefore, in the first multi-level model we included year as a proxy for a linear trend as well
as compressibility X year interaction as predictors of song success. Both year and lyrical com-
pressibility were mean-centered prior to analyses. This multi-level model showed a good over-
all model fit, R
2
= .05, with 3.9% of the variance explained by fixed effects. Results indicated a
significant effect of year, B= 0.318, SE = 0.031, t(df = 57.29) = 10.23, p<001, suggesting that
over time songs included in the sample on average had a lower chart rank—a typical regression
to the mean effect. Importantly, more compressible songs showed significantly higher rank in
the charts, B= - 9.321, SE = 0.661, t(df = 14640.88) = 14.10, p<.001, and this effect was partic-
ularly pronounced for more recent years, compressibility X Year interaction, B= - 0.105,
SE = 0.039, t(df = 14581.41) = 2.71, p= .007.
In the second step, we added mean-centered yearly music production index as a second
covariate, along with a music production X compressibility interaction. Based on prior auto.
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ARIMA results, we also included linear effect of year to account for the trend in the chart posi-
tion. This multi-level model also showed a good overall model fit, R
2
= .06, with 4.7% of the
variance explained by fixed effects. More compressible songs showed significantly higher rank
in the charts, B= - 9.353, SE = 0.657, t(df = 14819.95) = 14.23, p<.001. Also, average chart
position of songs was higher in years with a greater volume of songs produced, B= 6.141,
SE = 1.280, t(df = 53.76) = 4.80, p<.001. Moreover, as Fig 2 indicates, lyrical compressibility
was more strongly associated with song success in years with greater volume of produced
songs, compressibility X music production interaction, B= - 2.170, SE = 0.648, t
(df = 14781.15) = 3.35, p= .001. These analyses yield results consistent with the proposition
that lyrically simpler songs enjoy greater success in time periods in which more novel song
choices are available.
Forecasting
As a final step, we generated a forecast for average lyrical compressibility for four decades after
the last data point in our time series. This is in keeping with recommendations by Varnum &
Grossmann [38] that papers analyzing past patterns of cultural change provide forecasts for
the future. These forecasts enable a test of this theoretical model against concrete future cul-
tural trends. Using the automated ARIMA algorithm, we also identified the best function for
the novel song choices data, which we used to estimate the subsequent 40 data points. In turn,
we used this estimated data in conjunction with the compressibility function to forecast the
further development of lyrical compressibility. Results of this model suggest that lyrical com-
pressibility will continue to increase over the next several decades (see Fig 1).
Fig 2. Relationship between lyrical compressibility and chart position for years differing in music production volume.
Confidence bands indicate 95% around the estimate.
https://doi.org/10.1371/journal.pone.0244576.g002
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Discussion
Popular music lyrics have recently been used to inform work on the cultural transmission of
emotional expression [14,66], as an index of culture-level changes in self- versus other-focus
[5], and as a reflection of cultural mood in respond to economic and social threats [18,19]. But
one major trend in popular music lyrics remained underexplored and unexplained—popular
music lyrics are coming increasingly simple over time. We reasoned and found support for the
hypothesis that increasing lyrical simplicity is associated with increasing amounts of novel
music production. That is, in times when more novel music is produced, popular songs
become increasingly lyrically simple.
The relationship between mean lyrical compressibility and the amount of novel music pro-
duced each year was robust. We observed significant positive associations across three operatio-
nalizations of the amount of novel song choices and the average lyrical compressibility of
popular songs. Further, the relationship between amount of novel song choices and average com-
pressibility of popular songs remained significant when including a host of ecological, socioeco-
logical, and cultural factors linked to other types of cultural change both in univariate and
multivariate analyses. By and large these other variables were not significantly associated with
changes in lyrical simplicity after controlling for the potentially confounding influence of tempo-
ral autocorrelation. Of note, we also observed a significant negative association between changes
in pathogen prevalence and lyrical simplicity. This observation suggests a potentially new conse-
quence of infectious disease threat, one that should be explored in more detail in future work.
Importantly, the linkage between amount of new music produced and average compress-
ibility of popular songs also held when accounting for temporal autocorrelation using three
distinct methods. Thus, results suggest that the amount of novel music produced contributes
to changes in average lyrical compressibility above and beyond other plausible causes and
autoregressive trends in the data.
In exploratory analyses, we also found evidence suggesting that success, as indexed by posi-
tion in the billboard charts, among popular songs was associated with greater lyrical compress-
ibility. This is broadly consistent with the notion that simpler content enjoys an advantage in
memorability and/or transmission. Importantly, this effect appeared to be stronger in years
when the amount of novel songs produced was higher, providing conceptual confirmation of
our key finding. More novel song choices appear linked to both greater average lyrical compress-
ibility of the body of songs that succeeds (i.e., those entering the billboard chart in a given year),
and, among songs entering the charts in a given year, compressibility was more strongly associ-
ated with better performance on the chart in years when more novel songs were produced.
This finding might parallel ongoing research taking information-theoretic approaches in
exploring communicative efficiency in human language [67,68]. For example, in both language
and music, something akin to Zipf’s law seems to be at play [2]—i.e., the frequency rank of a phe-
nomenon is inversely proportional to its probability, such that, in the case of language, many
words are quite rare, but a few words (e.g., pronouns) appear with great frequency. Moreover,
these more successful (i.e., frequently-used) words tend be shorter in length (but see also Piantadosi
et al., 2011 [69]). This observation dovetails with our finding regarding the success of simpler lyrics.
Indeed, the increasingly success of simple lyrics may reflect increasing communicative efficacy.
A preference for simpler information in increasingly information-saturated environments
might also be consistent with some propositions from cultural evolutionary theory. One tenet
of cumulative cultural evolutionary theory is that human innovation, transmission, and learn-
ing increase the amount and quality of cultural information, while also increasing the learn-
ability of this information [70,25]. One way to increase information learnability is via
simplicity [71,72], thereby yielding increasingly efficient communication.
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The present report adds to two growing bodies of empirical research—work emphasizing
the examination of cultural products as a window into cultural-level psychological processes
[14,5] and work using time-series methods to test hypotheses regarding the causes of particu-
lar patterns of cultural change (for a review see Varnum & Grossmann, 2017 [38]). Here, we
use big data and time series methods to show that increases in the amount of novel songs over
time appear to be linked to the increasing simplicity of popular songs’ lyrics, as well as greater
success of songs with simpler lyrics. What does this tell us more broadly about how American
culture has changed? It suggests potentially that success of aesthetic complexity at the cultural
level may be something that shifts over time. Although this is not the first such demonstration
of this phenomenon, to our knowledge this is the first attempt to formally evaluate why such
cultural-level preferences may change.
Alternative and complementary explanations
Although we found that our key effect was highly robust, alternative or complementary expla-
nations for the growing success of lyrically simpler songs are still possible. For example,
changes in the ways that people consume popular music could perhaps affect lyrical simplicity.
Technological innovation (e.g., various portable music devices) could play a role, as could
other variation in the ways that people interact with music. Relatedly, one might speculate that
the success of increasingly simple lyrics might owe to technologically mediated increases in lis-
tening to music primarily in the background (e.g., on commutes, in gyms). However, one
might easily argue that for generations music has been consumed in this fashion albeit with
slightly different technologies—portable radios, car stereos, and portable music players have
existed and been widely used for decades. It would be interesting to attempt to assess this ques-
tion empirically, although we are not currently aware of high-quality time series data relating
to how and why people listen to popular music. Moreover, operationalization of these indica-
tors of technological innovations over time would be a potentially thorny problem. For
instance, what does it mean to own a Walkman in 1982 as compared to a similar device in
2002? Nonetheless, it would be intriguing to assess these questions in future work.
Another possibility is that the length of songs may have changed over time affecting average
lyrical complexity. Thus, perhaps song lyrics are more compressible by virtue of songs becom-
ing shorter. However, a recent analysis of songs entering the Billboard charts over the course
of its history suggests, in fact, that the average song on the charts in the late 2010’s was some-
what longer than those in the 1950’s and 1960’s, and similar in recent years to levels observed
in the 1970’s [73]. Thus, this alternative explanation cannot account for the trends observed in
the present analyses.
One might alternatively speculate that the rise in lyrical simplicity observed in the present
data might be related to trends in the popularity of different musical genres. Indeed, although
this is beyond the scope of the present work, it would be interesting to empirically assess how
lyrical complexity varies across popular music genres and whether trends within these genres
over time have been similar. Further, future work might assess whether the linkage between
lyrical simplicity and song success observed in our exploratory analyses varies within genres of
popular music or if genres that are on average simpler enjoy greater success in times of more
music production.
Limitations
It is worth noting that our analysis was restricted to a single type of cultural product. It might
be the case that empirical analysis of other domains might show similar trends and a similar
relationship between amount of novel content and success of simpler content, or it may be
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that different dynamics are observed when considering television shows, videogames, or other
types of cultural products. For example, many have argued that television shows have become
more complex and intellectually stimulating in the past few decades, entering the so-called
“Golden Age of Television.” However, empirical work examining complexity over time in
other types of cultural products, including movies, news broadcasts, print newspapers, novels,
and political speech suggests that there is in fact a broad trend toward simpler content being
increasingly preferred, at least when it comes to the language used in these products [74]. It is
noteworthy that Jordan and colleagues (2019) used a different measure of complexity, in this
case use of a specific set of words indicate cognitive complexity, and that they find that the
strength of the decline in complexity varies across different types of cultural products. Hence,
future research may attempt to conceptually replicate our work by assessing compressibility of
other types of cultural products over time and whether the success of such products is linked
to the number of options or alternatives within that domain.
It is also worth noting that, in the present work, we assessed the simplicity of lyrics. Songs
might be complex or simple in other ways as well, in terms of rhythm, melody, number of
instruments played, and so on. Analyses of these features is beyond the scope of the present
work, but it would be interesting to see the extent to which similar or divergent patterns are
observed in these facets of successful popular music over time.
Our analysis was also limited to songs that were relatively successful over time—i.e., those
that made the Billboard Hot 100 chart. This sample is quite large (N>14,000), but it may not
be representative of all songs produced during this period. Further, we were able to success-
fully scrape a greater proportion of more recent rather than older songs, which we included in
control analyses. Our sample captures a large chunk of popular music produced during more
than half a century and enables tests regarding linkages between novel music choices, lyrical
simplicity, and song success. A slightly different conceptual question may be worthwhile
addressing in future work: Does average complexity of all music produced change along with
shifts in the amount of music produced?
Our work is also limited by the fact that song success was operationalized by commercial
success in the US market. Although some cultural shifts in the past several decades appear to
be global in nature, such as rising individualism [36], this need not be the case for all dimen-
sions of culture. Different dynamics may potentially be observed in terms of song success in
parts of the world with different values, practices, and ecological conditions. Although such an
endeavor is beyond the scope of the present manuscript largely due to the lack of equally rich
time series data from other countries, it would be worthwhile to try to address this question in
the future.
Finally, the present work is limited by its correlational nature. Although our findings
appeared quite robust across different operationalizations of the independent variable—when
accounting for autocorrelation in various ways, and when controlling for a host of plausible
ecological, socioecological factors, and cultural values which have shifted over time—we can-
not completely rule out all alternative explanations for increasing success of songs with simpler
lyrics. Future work might attempt to quantify society level time series trends in conformity or
other biases linked to lyrical affect and music sampling [14,75], and assess whether the present
findings hold when controlling for these variables as well. Future work may also use in-lab
methods to explore and disentangle the possible causal mechanisms underlying the link
between amount of novel song choices and success of songs with simpler lyrics. For example,
transmission chain methods [76] could be employed to explore whether participants might
find simpler lyrics more pleasing and memorable when there is a greater number of other
song-snippets competing for attention versus when there is not.
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Conclusion
Why have the lyrics of pop songs become simpler over time? Our findings suggest that the
answer may have to do with the proliferation of new songs available to consumers. The present
work represents one of the first attempts to use big data and time series methods to quantify
temporal shifts in information transmission dynamics at the societal level. Future work may
attempt to replicate and extend these findings into other types of complexity and other types
of cultural products.
Supporting information
S1 Fig. Zero-order Kendall’s Tau correlations between variables.
(TIF)
S1 File.
(DOCX)
Author Contributions
Conceptualization: Michael E. W. Varnum, Jaimie Arona Krems.
Data curation: Colin Morris, Alexandra Wormley.
Formal analysis: Michael E. W. Varnum, Colin Morris, Igor Grossmann.
Investigation: Michael E. W. Varnum, Colin Morris, Igor Grossmann.
Methodology: Michael E. W. Varnum, Colin Morris, Igor Grossmann.
Resources: Colin Morris.
Validation: Alexandra Wormley.
Visualization: Alexandra Wormley, Igor Grossmann.
Writing – original draft: Michael E. W. Varnum, Jaimie Arona Krems, Colin Morris, Igor
Grossmann.
Writing – review & editing: Michael E. W. Varnum, Jaimie Arona Krems, Colin Morris, Alex-
andra Wormley, Igor Grossmann.
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