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RESEARCH ARTICLE
Cultural evolution of emotional expression in 50 years of
song lyrics
Charlotte O. Brand1* , Alberto Acerbi2and Alex Mesoudi1
1
Human Behaviour and Cultural Evolution Group, Biosciences, College of Life and Environmental Sciences, University of
Exeter, Penryn, UK and
2
Faculty of Science, Department for Early Prehistory and Quaternary Ecology, University of
Tübingen, Germany
*Corresponding Author: Human Behaviour and Cultural Evolution Group, Biosciences, College of Life and Environmental
Sciences, University of Exeter, Penryn TR10 9FE, UK. E-mail: c.brand@exeter.ac.uk
Abstract
Popular music offers a rich source of data that provides insights into long-term cultural evolutionary
dynamics. One major trend in popular music, as well as other cultural products such as literary fiction,
is an increase over time in negatively valenced emotional content, and a decrease in positively valenced
emotional content. Here we use two large datasets containing lyrics from n= 4913 and n= 159,015 pop
songs respectively and spanning 1965–2015, to test whether cultural transmission biases derived from
the cultural evolution literature can explain this trend towards emotional negativity. We find some
evidence of content bias (negative lyrics do better in the charts), prestige bias (best-selling artists are cop-
ied) and success bias (best-selling songs are copied) in the proliferation of negative lyrics. However, the
effects of prestige and success bias largely disappear when unbiased transmission is included in the mod-
els, which assumes that the occurrence of negative lyrics is predicted by their past frequency. We conclude
that the proliferation of negative song lyrics may be explained partly by content bias, and partly by undir-
ected, unbiased cultural transmission.
Keywords: cultural evolution; cultural transmission; popular music; sentiment analysis; transmission biases
Media summary: Increase in negative song lyrics and decrease in positive in 50 years of pop music
probably due to cultural drift.
Introduction
Are the lyrics of contemporary pop songs happier or sadder than the lyrics of the popular songs of
previous generations? Do current songs mention love more or less than they used to? Are the pop
charts angrier now than in the past, or have they mellowed over time? Millions of people buy and lis-
ten to popular music every day (by ‘popular music’or ‘pop music’, we mean music with wide appeal
that is typically distributed to large audiences through the music industry, and not just the specific
‘pop’genre), and their song choices offer a window into their emotional states and psychological pre-
ferences. Changing trends in pop music over time may therefore offer a means of measuring
large-scale societal changes. In recent years, the availability of large datasets in electronic format
has allowed long-term, population-level cultural dynamics to be identified in an increasingly precise,
quantitative way (Michel et al. 2011). This, in turn, permits researchers to test hypotheses about cul-
tural trends that previously could only be assessed informally by focusing on a small number of
(potentially cherry-picked) cases.
© The Author(s) 2019. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://crea-
tivecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original
work is properly cited.
Evolutionary Human Sciences (2019), 1, e11, page 1 of 14
doi:10.1017/ehs.2019.11
A fruitful area of investigation concerns the analysis of emotions in human cultural expressions.
Several tools have been developed to extract the emotional content of texts, known as ‘sentiment ana-
lysis’. Some of these provide a classification of how words score on ‘positive’and ‘negative’content
(Baccianella et al.2010), and others provide additional scores for specific emotions (e.g. how
‘angry’or ‘sad’is a text; Pennebaker et al.2007). In most cases, sentiment analysis has been applied
on a short-term time scale, such as social media interactions (Lansdall-Welfare et al. 2012). However,
some researchers have explored a longer time scale, analysing the expression of emotions in several
decades of song lyrics (Dodds and Danforth 2010), newspaper articles (Iliev et al.2016), in
Grimm’s folktales (Mohammad 2013) or in centuries of literary works (Acerbi et al.2013).
The quantitative description of trends is fundamental, but a further necessary step is to understand
what drives these trends. Cultural evolution theory (Boyd and Richerson, 1985; Cavalli-Sforza and
Feldman, 1981; Mesoudi 2011) provides a series of concepts that allows such an endeavour.
Drawing a parallel with genetic evolution, this field focuses on how cultural variation is transmitted
from person to person via social learning processes such as imitation, and the processes that change
that transmitted variation over time. In particular, cultural evolutionists have focused on transmission
or learning biases as key drivers of cultural evolutionary dynamics (R. L. Kendal et al. 2018; Rendell
et al. 2010). Transmission biases are heuristics that individuals use to decide what, when and from
whom to copy. They are rule-of-thumb principles such as ‘copy the majority’,‘copy the successful’
or ‘copy the prestigious’that allow individuals to adaptively learn from others (Laland 2004).
Importantly, different transmission biases give rise to different population-level cultural dynamics.
A cultural trait introduced in a population in which, for example, individuals copy mostly from
their parents will spread slower than the same cultural trait introduced in a population in which indi-
viduals copy mostly from a few successful or prestigious individuals (Cavalli-Sforza and Feldman,
1981).
‘Model-based’biases describe from whom people learn: for example, a success bias describes a
tendency to learn from successful others, and a prestige bias a tendency to learn from prestigious
(high status, respected) others. ‘Content-based’biases describe what kind of information people
learn best, owing to its salience or memorability. For example, a bias to transmit emotionally salient
content, or negative content, has been found in several laboratory experiments (Bebbington et al. 2017;
Fessler et al. 2014; Stubbersfield et al. 2015,2017). These biases can be compared with unbiased trans-
mission, in which cultural variants are transmitted in equal proportion to their existing frequency in
the population. While there are many theoretical models that examine the conditions under which
different transmission biases are adaptive and their expected population-level consequences (J.
Kendal et al. 2009; Rendell et al. 2010), and experiments which have tested the predictions of these
models in controlled laboratory set-ups (Caldwell and Millen 2008; Mesoudi 2008; Mesoudi and
O’Brien 2008; Morgan et al. 2012), less research has explored how cultural transmission biases may
impact real-life cultural dynamics (although see Acerbi and Bentley 2014; Beheim et al. 2014; Miu
et al. 2018).
In this study, we test the extent to which transmission biases can explain trends in the emotional
content of two datasets of English language song lyrics. The first dataset (‘billboard’) contains the
lyrics of the songs included in the annual Billboard Hot 100 from 1965 to 2015, a widely known
US chart (n= 4913 songs). The second dataset (‘mxm’) contains the lyrics of the English language
songs present in the musixmatch.com website, the world’s largest lyrics platform where users can
search and share lyrics, from 1965 to 2010 (n= 159,015 songs).
Preliminary analyses found a substantial decrease in the use of positive emotion-related words
coupled with an increase in the use of negative emotion-related words in both datasets. Specific
emotion-related words show considerable change in use during the time frame considered. For
example, use of the term ‘love’more than halved in both datasets, whereas the term ‘hate’increased
in frequency substantially (see Figure 1 for the ‘billboard’dataset. The trends are qualitatively similar
in the mxm dataset). These results are broadly consistent with previous analyses of song lyrics (DeWall
2 Charlotte O. Brand, Alberto Acerbi and Alex Mesoudi
et al. 2011; Dodds and Danforth 2010) and literary fiction (Morin and Acerbi 2017), suggesting a gen-
eral cultural or artistic trend for increasingly negatively valenced emotional expressions.
The main goal of this study is to test hypotheses about possible drivers of these two trends. Before
testing for the aforementioned transmission biases, we first checked whether linguistic effects could
explain the patterns. We considered (a) a possible increase in slang words, (b) an asymmetric semantic
change (for which words denoting negative emotions had acquired positive or neutral connotation,
e.g. ‘wicked’, but not vice versa) and (c) a general increase in lyric complexity (although note the latter
would predict that both negative- and positive-emotion words would decrease in frequency). After
finding that the trend persisted after controlling for these linguistic effects (see Supplementary
Material), we then examined whether cultural transmission biases might explain the patterns.
We considered, in a fully preregistered study, three hypotheses derived from cultural evolution the-
ory as outlined above:
(H1) Success bias: the emotional trends result from artists copying the best-selling songs from
the preceding years.
(H2) Prestige bias: the emotional trends result from artists copying the songs of ‘prestigious’
artists (independently of the success of the songs) from the preceding years.
(H3) Content bias: there is a general psychological preference for lyrics that reflect negative
emotions in songs, thus songs with more negative content rank higher in the charts.
Note that we applied H1 and H2 to both positive and negative content, while H3 was applied only to
negative content. This was because, although there is ample evidence for a content bias towards nega-
tive emotion, as referenced above, we are not aware of any evidence or theory that would predict a
content bias for positive emotions.
After running our preregistered analyses to test these three preregistered hypotheses and depositing
a preprint, we received the useful suggestion to also include an additional variable to control for
unbiased transmission. Including this additional variable affected the interpretation of our results.
Figure 1. Proportion of the term ‘love’(left panel) and ‘hate’(right panel) in all song lyrics by year for the dataset billboard which
contains the lyrics of the songs included in the annual US Billboard Hot 100 (n =4913 songs). The proportions here are small as we
are reporting the proportion of the word out of the total number of words in 100 songs each year (on average 30,000 words, i.e. 300
words/song) and on different scales (the frequency of positive emotion words is usually higher than the frequency of negative emo-
tion words). To have an intuitive idea of the change, from 1965 to 1990, in the top-100 billboard songs, the word ‘hate’was used
each year around four or five times overall (30,000*0.00015), whereas now the average is around 24 (30,000 × 0.0008).
Evolutionary Human Sciences 3
Hence below we report both the preregistered analyses without unbiased transmission and the revised
analyses including unbiased transmission. The use of unbiased transmission here assumes that the
emotional trends of lyrics result from artists randomly copying the lyrics of any of the available
songs in the preceding years, without taking into account chart rank or artist prestige.
Our results are mixed. We found a small effect of success bias on the likelihood of a word being
positive in the billboard dataset, as well as a small effect of both prestige and content bias on the like-
lihood of a word being negative in the billboard dataset. However, these effects vastly reduced or
became non-existent in the mxm dataset. Moreover, when controlling for unbiased transmission,
the effects of success and prestige largely disappeared in both datasets. We therefore conclude that
content bias may play a role in the likelihood of using negative emotion words in song lyrics, but
that success and prestige biases (as we have implemented them) are not strong enough to explain
the trends compared with unbiased transmission. We discuss possible explanations for the apparent
content bias effect, as well as our interpretation and implementation of success, prestige and unbiased
transmission in relation to the content of song lyrics.
Methods
Data preparation
Dataset ‘mxm’
We downloaded (in August 2017) the bag-of-words data (n= 237,662 songs) from https://labrosa.ee.
columbia.edu/millionsong/musixmatch. We used the mxm track ID to retrieve from musixmatch.com
the name of the artist(s), the year of first release and the genre for each song.
From this dataset we eliminated songs for which it was not possible to retrieve the year or the
artist, as well as songs that did not appear to be in English. To assess if a song was in English we
checked whether the word ‘the’was present at least once. The new dataset contained n= 163,551
songs. We additionally filtered the dataset by retaining only the years for which more than 500
songs were present, thus obtaining our final sample of n= 159,015 songs, ranging from 1965 to
2010, both included.
Artists’names were further processed using the cluster function in R library refinr (Muir 2018)to
cluster and merge similar names (e.g. ‘madonna’,‘Madonna’,‘MADONNA’). To disambiguate colla-
borations we looked for standard separators in artist names (e.g. ‘featuring’,‘feat.’,‘feat’,‘and’,‘AND’,
‘and’,‘with’,‘,’). We considered artists where no separators were found as single artists. We then ran
the strings where separators were found (e.g. X and Y) through the list of single artists. If both X and Y
were found in this list, then X and Y were considered as single artists occasionally collaborating (e.g.
Eminem and Dr. Dre), if not, they were considered a stable collaboration, and a single data point in
our models (e.g. Simon and Garfunkel).
Dataset ‘billboard’
We downloaded (in May 2017) the data from https://github.com/walkerkq/musiclyrics (n= 4913
songs). As the lyrics in the mxm dataset are stemmed (a common practice in digital text analysis:
words are reduced to their stems, roughly analogous to their morphological roots, e.g. ‘happily’,
‘happy’, and ‘happiness’are all reduced to the stemmed form –‘happi’), we processed the ‘billboard’
dataset in the same way, re-adapting the script used to process the mxm dataset: https://github.com/
tbertinmahieux/MSongsDB/blob/master/Tasks_Demos/Lyrics/lyrics_to_bow.py. Henceforth when we
use the term ‘word’or ‘lyric’we are referring to the stemmed version.
Artists were further processed in the same way as the mxm dataset. Notice the ‘genre’entry is not
present in this dataset as this information was not provided by the Billboard data source, but,
importantly, chart position is, which we coded as ‘rank’.Rankisthesong’s chart position, from
1 to 100, in the yearly Billboard list, where 1 indicates the best-selling and 100 indicates the least-
selling song.
4 Charlotte O. Brand, Alberto Acerbi and Alex Mesoudi
Sentiment analysis
We used the ‘positive emotions’and ‘negative emotions’categories of the text analysis application
Linguistic Inquiry and Word Count (Pennebaker et al. 2007). These categories are ‘virtually
unchanged’compared with the most recent (2015) version of LIWC. The words were stemmed as
described above, and we analysed the lyrics with n= 267 negative emotion stems and n= 169 positive
emotion stems. Thus each word of each song was classified as positive, negative or neither. As noted in
the introduction, the practice of analysing the emotional valence of texts using single words as units is
common and prevalent in natural language processing studies (a recent review is Ribeiro et al. 2016).
Data analysis
Statistical approach
We used Bayesian, aggregated binomial, multilevel models, to examine the effect of prestige, success
and content bias on the likelihood of any given word being either positive or not, and negative or
not. We conducted all analyses in R version 3.6.0 using the Rethinking package (R Core Team
2019; McElreath 2016). We compared WAICs (‘Widely Applicable Information Criteria’, see
McElreath 2016) in a model comparison approach as we stated in our preregistration. WAICs can
be thought of as a type of information criteria, such as the better-known AIC, but one that uses
the entire posterior distribution for estimation rather than a single point estimate. However, it is
important to note that we interpreted the parameter estimates of the full models in all cases, based
on advice regarding the instability of WAIC estimates for our time series data. Model parameters
were said to have an effect on the model outcome if their 89% credible interval did not cross zero
(NB. 89% is the default value in the rethinking package). Priors were chosen to be weakly regularising,
in order to control for both under- and overfitting of the model to the data. Owing to the large num-
ber of data points that each model processed (each model of the mxm dataset processed roughly 29
million words), and skewed samples within genre and artist categories, we had to reparameterise
and adapt the models accordingly to ensure sufficient and appropriate convergence. Trace plots, effect-
ive sample sizes and Rhat values were used to check for appropriate model convergence throughout.
Full analysis scripts and data are available at www.github.com/lottybrand22/song_lyrics, and were
preregistered at https://osf.io/94ubt/
Level of analysis
Each word of a song was coded, according to LIWC, as ‘positive’(when present in the list of positive
stems), ‘negative’(when present in the list of negative stems) or neither. Thus, we implemented sep-
arate models for coding positive and negative lyrics (see Tables 1 and 2). Our models are aggregated
binomial models, in that the words are aggregated within songs, but each word of a song is modelled
as the binomial probability of being positive (or not). The negative models model the likelihood that
each word in a song is negative (or not). This takes into account the fact that each song has a different
number of words, and negates any need for averaging over words and songs. Each song is not an inde-
pendent data point, as the data are clustered on artists, genre and year of release. Thus, we implemen-
ted varying effects models, allowing adaptively regularising priors for the intercepts of artist, genre and
year of release.
Hypotheses
See Table 1 for specifications of all models. The success bias models assume that the probability that
any given word in a song is positive (or negative) can be predicted by the average number of positive
(or negative) words of the top-10 songs in the preceding three years of the billboard list. Thus our
variable ‘success’for both mxm and billboard datasets consisted of the average number of positive
(or negative) words from the top-10 songs of the billboard dataset, for the preceding three years of
the song of interest.
Evolutionary Human Sciences 5
The prestige bias models assume that the probability that any given word in a song is positive
(or negative) can be predicted by the average number of positive (or negative) words of the songs
of prestigious artists in the preceding three years. We define ‘prestigious’artists as those that appeared
more than 10 times in the Billboard top-100. This results in 86 ‘prestigious’artists (less than 4% of the
total in the billboard dataset).
The content bias models assume that the probability that a word of a song will be negative is pre-
dicted by the chart positions of the song in the billboard charts, which we assume reflects a psycho-
logical bias, or preference for, negative content in songs.
A control for unbiased transmission is implemented by including an effect for the average number
of positive (or negative) words of all songs in the preceding three years. This assumes that the prob-
ability that any given word in a song is positive (or negative) increases as the frequency of positive (or
negative) words in the population of recent songs increases.
Note that many of these choices are necessarily subjective (e.g. using the top-10 songs for success
bias, or more than 10 appearances in the charts for prestige). We invite readers to explore alternative
choices by adapting our analysis scripts for use on the openly released data.
Results
Model comparison
Our model comparison results suggested that, although some models hold the majority of the weight
for the billboard models, it is important to note that their WAIC values are not very different from one
another, and the standard errors of the differences show that there is a lot of uncertainty in the model
comparison. Furthermore, model comparison is less robust when modelling time-series data.
Therefore, we are reporting the results of the full models that include all of the effects of interest,
while noting that the model comparison results should be interpreted with caution. We first report
Table 1. Details of model comparison results for the billboard dataset models. The model with the lowest WAIC and
highest proportion of the WAIC weight from each set is in bold. Please note that we always used the full models for
inference, as the WAIC standard errors contain a lot of overlap, see Results section for more information
Negative/
positive lyrics Model Parameters WAIC Weight SE
+ve Null Artist|1 + Year|1 646946.3 0.12 1676.99
+ve Success bias Success + Artist|1 + Year|1 646942.50.79 1677.00
+ve Prestige bias Prestige + Artist|1 + Year|1 646948.8 0.03 1677.01
+ve Full Success + Prestige + Artist|1 +
Year|1
646947.9 0.05 1676.93
+ve Full + unbiased
transmission
Success + Prestige + unbiased
+ Artist|1 + Year|1
646952.2 0.01 1676.96
−ve Null Artist|1 + Year|1 359482.2 0.00 1442.19
−ve Success bias Success + Artist|1 + Year|1 359488.9 0.00 1442.21
−ve Prestige bias Prestige + Artist|1 + Year|1 359493.1 0.00 1442.20
−ve Content bias Chart position + Artist|1 +
Year|1
359472.2 0.22 1442.17
−ve Full Success + Prestige + Chart
position + Artist|1 + Year|1
359466.40.77 1442.21
−ve Full + unbiased
transmission
Success + Prestige + Chart
position + unbiased +
Artist|1 + Year|1
359479.6 0.01 1442.21
6 Charlotte O. Brand, Alberto Acerbi and Alex Mesoudi
the full models without a parameter for unbiased transmission as was originally preregistered. We then
report how the results change when controlling for unbiased transmission.
Models of positive lyrics using the billboard dataset
When modelling the likelihood that any word in the lyrics of a song is positive, the full model (Fig. 2a)
suggested that success had a small positive effect (mean coefficient estimate: 0.04, 89% confidence
interval (CI) [0.00, 0.07], however prestige did not have a detectable effect (0.02, CI [−0.01, 0.06]).
These effects are on the log-odds scale, and thus can be interpreted as the odds that a given word
is positive increases by 4% as the number of positive words in the previous three years of top-10
songs increases. The model comparison suggests support for H1, suggesting that our hypothesis
that success bias contributes to the proportion of positive lyrics was best supported, although there
is a lot of uncertainty around the models’WAIC estimates. However, when controlling for unbiased
transmission, the updated full model (Fig. 2b) suggested that neither success nor prestige had a detect-
able effect (−0.03, CI [−0.07, −0.01], −0.01 CI [−0.04, 0.02]), but that our implementation of
unbiased transmission predicted the likelihood of a given word in the lyrics of a song being positive
(0.12, CI [0.07, 0.16]).
Models of negative lyrics using the billboard dataset
When modelling the likelihood that any word in the lyrics of a song is negative, the full model (Fig. 2c)
suggested that prestige had a positive effect (mean coefficient estimate: 0.08, 89% CI [0.01, 0.14]; how-
ever success did not have a strong effect (0.01, CI [−0.05, 0.07]). Content bias had a negative effect,
Table 2. Details of model comparison results for the mxm dataset models. The model with the lowest WAIC and highest
proportion of the WAIC weight from each set is in bold. Please note that we always used the full models for inference, as
the WAIC standard errors contain a lot of overlap, see Results section for more information
Negative/
positive lyrics Model Parameters WAIC Weight SE
+ve Null Artist|1 + Genre|1 + Year|1 9481478 1 6651.86
+ve Success bias Success + Artist|1 + Genre|1 +
Year|1
9481503 0 6651.80
+ve Prestige bias Prestige + Artist|1 + Genre|1 +
Year|1
9481502 0 6651.80
+ve Full Success + Prestige + Artist|1 +
Genre|1 + Year|1
9481518 0 6651.89
+ve Full + unbiased
transmission
Success + Prestige +
unbiased + Artist|1 +
Genre|1 + Year|1
9481500 0 6651.79
−ve Null Artist|1 + Genre|1 + Year|1 6602738 1 6057.61
−ve Success Success + Artist|1 + Genre|1 +
Year|1
6602763 0 6057.55
−ve Prestige Prestige + Artist|1 + Genre|1 +
Year|1
6602754 0 6057.52
−ve Full Success + Prestige + Artist|1 +
Genre|1 + Year|1
6602787 0 6057.53
−ve Full + unbiased
transmission
Success + Prestige +
unbiased + Artist|1 +
Genre|1 + Year|1
6602767 0 6057.54
Evolutionary Human Sciences 7
meaning that songs that had a numerically lower chart position (i.e. closer to the top of the charts at 1
rather than 100) increased the log odds of a lyric being negative (−0.02, CI [−0.04, −0.01]). When
controlling for unbiased transmission (Fig. 2d), the model suggested that neither success nor prestige
had a detectable effect on the likelihood of a lyric being negative (0.01, CI [−0.05, 0.06], 0.04 CI
[−0.04, 0.12]). However content bias retained its effect, in that a lower chart position increased the
likelihood of a lyric being negative (−0.02, CI [−0.04, −0.01]). There was not strong evidence of
unbiased transmission (0.06, CI [−0.02, 0.13]).
Models of positive lyrics using the mxm dataset
When modelling the likelihood that any word in the lyrics of a song in the mxm dataset is positive
(Fig. 3a), the full model suggested that prestige had a small positive effect (mean coefficient estimate
0.01, 89% CI [0.00, 0.02]; however success did not have an effect (0.00, CI [−0.01, 0.01]). The prestige
effect is much smaller than those from the billboard models. The model comparison suggested that the
best model was the null model, suggesting that our parameters for success and prestige did not
Figure 2. (a) Parameter estimates from the full positive billboard model. Estimates include 89% confidence intervals. Estimates
that cross zero are interpreted as not having a strong effect on the probability of a lyric being positive. (b) Parameter estimates
from the full positive billboard model with unbiased transmission included. (c) Parameter estimates from the full negative billboard
model. (d) Parameter estimates from the full negative billboard mode with unbiased transmission included.
8 Charlotte O. Brand, Alberto Acerbi and Alex Mesoudi
improve the model fit more than simply including the varying effects for artist, genre and year of
release. Furthermore, when controlling for unbiased transmission (Fig. 3b) we found no effect of pres-
tige and success (0.00, CI [0.00, 0.01], −0.01 CI [−0.02, 0.00]) and weak evidence of unbiased trans-
mission (0.01, CI [0.00, 0.03]).
Models of negative lyrics using the mxm dataset
When modelling the likelihood that any word in the lyrics of a song is negative, the full model (Fig. 3c)
suggested that neither prestige nor success had an effect (prestige 0.00, 89% CI [−0.02, 0.01]; success
0.00, CI [−0.01, 0.01]). Furthermore, the model comparison indicated that the best model was the null
model, suggesting that our parameters for success and prestige did not improve the model fit more
than simply including the varying effects for year, artist, genre. Similarly, when controlling for
unbiased transmission (Fig. 3d) we found no effect of prestige and success, (−0.02, CI [−0.03,
0.01], 0.00 CI [−0.01, 0.01]) and weak evidence of unbiased transmission (0.02, CI [0.00, 0.03]).
Discussion
We analysed the emotional content of song lyrics in over 160,000 songs spanning the years 1965–2015.
We found that the frequency of negative words increased over time, whilst the frequency of positive
words decreased over time, and asked whether these patterns could be attributed to cultural transmis-
sion biases such as success bias, prestige bias, content bias or unbiased transmission. In the billboard
dataset, containing top-100 songs from 1965 to 2015, we found an effect of unbiased transmission on
Figure 3. (a) Parameter estimates from the full positive mxm model. Estimates include 89% confidence intervals. Estimates that
cross zero are interpreted as not having a strong effect on the probability of a lyric being positive. (b) Parameter estimates from the
full positive mxm model with unbiased transmission included. (c) Parameter estimates from the full negative mxm model. (d)
Parameter estimates from the full negative mxm model with unbiased transmission included.
Evolutionary Human Sciences 9
positive lyrics, and an effect of content bias on negative lyrics. For the larger mxm databases we only
found weak effects of unbiased transmission for both negative and positive lyrics.
The effects we found in all models are extremely small. This is partly because we analysed the data
on the scale of each word, negating any need for averaging over lyrics and songs. Thus, the relative
increase or decrease in the log odds is understandably small. Furthermore, our implementation of
transmission biases is necessarily indirect and simplified given that we lack direct observations of
song lyrics being copied. It is therefore unsurprising that the effects vastly reduced or disappeared
when controlling for unbiased transmission, given how many other factors must be at play in the gen-
eration of song lyrics, both directional biases such as those we explored here and random processes
(Bentley et al. 2007). For example, prestige can be realised in myriad ways (Jiménez and Mesoudi
2019), particularly in the music industry. The effect of various recording companies, the extent of
media attention outside of the charts and the amount of money spent on music promotion may all
play a significant role in an artist’s apparent prestige, and is not necessarily restricted to the content
of their music. Our implementation of ‘prestige’as predominance in the charts therefore only captures
one specific aspect of musical prestige.
The effect of unbiased transmission is, however, the largest and most consistent in all of our mod-
els. This result suggests there may be an effect of random drift, or random copying, in the emotional
content of song lyrics over time. This is consistent with previous work showing that random copying
can explain changes in the popularity of dog breeds, baby names and popular music (Bentley et al.
2007; Hahn and Bentley 2003), as well as archaeological pottery and technological patents (Bentley
et al. 2004). Thus, rather than song-writers being influenced by the most prestigious or successful
artists, they may simply be influenced by the emotional content of any of the available song lyrics
in the previous timestep, which may happen to increase in negativity or decrease in positivity
owing to small fluctuations. As in previous work, our results do not provide evidence of literal random
copying by individuals as we do not have direct access to individual’s copying decisions. Instead, ran-
dom drift is posed as a baseline against which to compare evidence of other copying biases. It is pos-
sible that the population-wide patterns are not a result of unanimous random copying, but owing to a
multitude of idiosyncratic causes that collectively cancel each other out to create the appearance of
random copying (Hoppitt and Laland 2013). In this sense, any small fluctuation in negative words
owing to a particular historical event, or owing to the emergence of a more negatively biased genre,
may have caused an initial increase in negative lyrics, which became exacerbated by random drift.
The presence of a content bias in the likelihood of negative lyrics occurring in the billboard songs is
noteworthy. This result suggests that songs with more negative lyrics are more successful in general,
perhaps reflecting either a general negativity bias (Bebbington et al. 2017; Fessler et al. 2014)oran
art-specific, or music-specific, negativity bias. Similar trends favouring negative emotions vs positive
ones in other artistic domains support our finding. As mentioned above, Dodds and Danforth (2010)
documented a decrease in frequency of positively valenced words, and an increase in negatively
valenced ones in pop song lyrics (a similar result was found in DeWall et al. 2011). Morin and
Acerbi (2017) found a similar pattern in centuries of literary fiction, with a general decrease in the
frequency of words denoting emotions, explained by a decrease in words denoting positive emotions,
whereas the frequency of negative words remained constant. It is worth noting that we were unable to
look for content bias (with our implementation) in the mxm data as there was no ranking system. One
possible way of determining the popularity or use of a song could be to look at how many times, or
how often, its lyrics are searched for, and whether this correlates with negative content.
In general, the idea that negative emotions would be privileged in art is consistent with the hypoth-
esis that artistic expressions may have an adaptive function, in particular as simulation of social inter-
actions (Mar and Oatley 2008). According to this view, developed with literary fiction in mind but
potentially generalisable to other expressive forms, art would provide hypothetical scenarios where
we can test and train, with no risk, our cognitive and emotional reactions. From this perspective, simu-
lating negative events is more useful than simulating positive ones (Clasen 2017; Gottschall 2012). Art
expressing negative emotions, in addition, may hold more value for audiences seeking comfort from
10 Charlotte O. Brand, Alberto Acerbi and Alex Mesoudi
the knowledge that others also experience negative emotions. Indeed, studies have shown that people
underestimate the prevalence of others’negative emotions, and this underestimation exacerbates lone-
liness and decreases life satisfaction (Jordan et al. 2011). Furthermore, suppressing rather than
reappraising negative emotions decreases self-esteem and increases sadness (Nezlek and Kuppens
2008)(Nezlek & Kuppens, 2008). This hypothesis is worth investigating in future research.
Our varying effects models suggested that most of the variation lay between artists. However, genre
also showed considerable variation. We were unable to control for genre in the billboard data as genre
information was not available with this dataset. This could provide a partial explanation for the differ-
ing results between the billboard and mxm datasets; indeed, Dodds and Danforth (2010) attributed the
decrease in emotional valence within pop song lyrics to the emergence of more negative genres such as
heavy metal and punk. Future work investigating the variation of emotional expression between dif-
ferent genres of music would be valuable. A further limitation of this study is that we restricted our
analysis to comparing the content of each song with that of the songs from the previous three
years of songs. Mechanistically this suggests that songs that are currently in the charts influence song-
writers who are writing within three years of chart success, assuming that the time it takes to get from
the song-writing process to chart success is three years or less. It is possible that these effects are stron-
ger or weaker at different time points, such as within one or five years of chart success. Furthermore,
although we controlled for artist, many songs in the billboard charts are in fact written by specially
designated song-writers, such as Max Martin.
Overall this research contributes to the growing body of work attempting to quantitatively study
trends in the domain of music (Youngblood 2019; Savage 2019; Mauch et al. 2015; Ravignani et al.
2017). Our starting result of an increase in negative emotions and decrease in positive ones in song
lyrics is paired with similar findings regarding acoustic qualities. Using the same Billboard top-100
songs that we analysed, Schellenberg and von Scheve (2012) found an increase in minor mode and
a decrease in the average tempo, which indicates that the songs become more sad-sounding through
time. This seems to be part of a longer trend in Western classical music, where the use of the minor
mode increased over a 150-year period from 1750 to 1900 (Horn and Huron 2015). The relationship
between minor tone and negative valence of lyrics has been also studied, and confirmed, quantitatively
(Kolchinsky et al. 2017). Analogously, studying more than 500,000 songs released in the UK between
1985 and 2015, Interiano et al. (2018) found a similar decrease in ‘happiness’and ‘brightness’, coupled
with a slight increase in ‘sadness’(these high-level features result from algorithms analysing low-level
acoustic features, such as the tempo, the tonality, etc.). They also found the puzzling result that, despite
a general trend towards sadder songs, the successful hits are, on average, happier than the rest of the
songs. In the same way, whereas we found that the higher the position in the billboard chart the more
negative a song is, billboard songs are as a whole more positive than the songs in the mxm dataset,
which contains more (and less successful) songs.
In this study we used cultural evolutionary theory to try to explain patterns in one of the most
pervasive of human cultural practices, music production. More specifically, we tried to detect
whether any particular transmission bias best explained the changing patterns of emotional expres-
sion over time. We conclude that, although we found weak evidence of success and prestige biases,
these were overwhelmed by an effect for unbiased transmission. The presence of a content bias for
negative lyrics remained, and this may be a contributing factor to the increasing in negative lyrics
over time. A potential explanation for these results is that a multitude of transmission biases and
other causes are at play. It is likely that small shifts, for example owing to historical events or the
emergence of particular genres, may have nudged the production and transmission of negative and
positive lyrics in opposite directions, and random copying exacerbated this trajectory. These pos-
sibilities should be explored more in future work. Overall, the exercise of precisely analysing large
datasets to explain cultural change, if refined on relatively benign cultural trends such as pop music,
could eventually be more expertly applied to areas of greater societal importance and impact, such
as shifts in political beliefs or moral preferences.
Evolutionary Human Sciences 11
Supplementary Material. The supplementary material for this article can be found at https://doi.org/10.1017/ehs.2019.11
Acknowledgements. We would like to thank the following people who contributed valuable advice for this project. Dr Sally
Street provided feedback on our preprint and suggested controlling for unbiased transmission in our full models; we are very
grateful for this suggestion as it was a valuable addition to our final analyses. We are grateful to Professor James Pennebaker
who answered our questions regarding the latest version of the LIWC programme. We are also grateful to Professor Richard
McElreath who advised us on many aspects of our analyses.
Author contributions. AA retrieved the data and completed the initial exploration. AA, AM and CB designed the project
and preregistration together. CB and AA wrote the analysis scripts. CB ran the analyses. CB AA and AM interpreted the
results and wrote the manuscript together.
Funding. This research was supported by The Leverhulme Trust (grant no. RPG-2016-122 awarded to AM).
Publishing ethics. The authors declare that this work is in accordance with Cambridge University Press’s publishing ethics
guidelines, and is original and has not been published elsewhere, although it is available as a preprint on SocArXiv at https://
osf.io/preprints/socarxiv/3j6wx/ and was fully preregistered at https://osf.io/wr3qm/registrations
Conflict of interest. The authors declare no conflicts of interest.
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Cite this article: Brand CO, Acerbi A, Mesoudi A (2019). Cultural evolution of emotional expression in 50 years of song
lyrics. Evolutionary Human Sciences 1, e11, 1–14. https://doi.org/10.1017/ehs.2019.11
14 Charlotte O. Brand, Alberto Acerbi and Alex Mesoudi
