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Studies investigating the temporal evolution of lyrics predominantly focus on tracing emotional cues over the years. For instance, Dodds et al.17 identify a downward trend in the average valence of song lyrics from 1961 to 2007. Napier and Shamir21 investigated the change in sentiment of the lyrics of 6150 Billboard 100 songs from 1951 through 2016. They find that positive sentiments (e.g., joy or confidence) have decreased, while negative sentiments (e.g., anger, disgust, or sadness) have increased. Brand et al.13 use two datasets containing lyrics of 4913 and 159,015 pop songs, spanning from 1965 to 2015, to investigate the proliferation of negatively valenced emotional lyrical content. They find that the proliferation can partly be attributed to content bias (charts tend to favor negative lyrics), and partly to cultural transmission biases (e. g., success or prestige bias, where best-selling songs or artists are copied). Investigating the lyrics of the 10 most popular songs from the US Hot 100 year-end charts between 1980 and 2007, DeWall et al.18 find that words related to oneself (e.g., me or mine) and words pointing to antisocial behavior (e.g., hate or kill) increased while words related to social interactions (e.g., talk or mate) and positive emotions (e.g., love or nice) decreased over time.
Overview of data and analyses performed. Based on a wide variety of descriptors capturing the lyrical characteristics from listening data and lyrics content, we perform two analyses. Analyses 1 identifies descriptors that are characteristic of the release year and genre. Analyses 2 investigates the relationship between the identified lyrics descriptors, popularity (listening counts and lyrics view counts), and release year.
Figure 1 provides an overview of the methodological framework used for the analyses presented. The two analyses conducted aim to (1) investigate the evolution of descriptors over five decades by performing a release year regression task to identify the importance of descriptors, and (2) investigate the interplay of lyrics descriptors, release year, and lyrics view count by performing a regression analysis on a dataset containing 12,000 songs, balanced for both genres and release years. The combination of these two analyses provides us with complementary findings; while the first analysis uses the entirety of our collected dataset and therefore derives general findings on descriptor importance, the second analysis, performed on a carefully balanced, reduced dataset, provides us with a more in-depth analysis on the strength of relationships of the individual lyrics and popularity descriptors and temporal aspects.
A total of 2400 items, i. e., songs, are considered for each musical genre. Due to the high diversity across the measurement unit of the predictors, i. e., popularity scores and lyrics descriptors, these are z-score normalized and multicollinear outliers are identified by computing Mahalanobis distance46 and subsequently removed. Highly correlated descriptors are also discarded until all of them presented a variance inflation factor less than 5. The results from the multinomial logistic regression show that lyrics view count differs across decades for the evaluated genres. Therefore, we investigate the relation between lyrics view count and particular lyrics descriptors by also fitting a multiple linear regression model containing the interaction between the lyrics view count and the other predictors. However, the model with the interaction is not significantly better than the baseline model (for all the musical genres, analysis of variance yields \(p>.01\)); thus, only the model without interaction is considered in the evaluation of the multiple linear regression results for each genre. The statistical models of Analysis 2 are built on the statistical software R47 version 4.1.2 (2021-11-01). Multinomial logistic regression is carried out using the mlogit package48 (version 1.1-1) while the linear models for each genre are fitted with the nlme package49 (version 3.1-155) and multiple comparisons across genres are performed with the multcomp package50 (version 1.4-25). The graphic shown in Fig. 3 is generated with the ggplot2 package51 (version 3.4.3).
Figure 2 shows the distribution of descriptor values for repeated line ratio and ratio of choruses to sections over time, separately for each of the five genres. Each genre is analyzed separately, with a robust regression model trained for each descriptor-genre combination; the resulting regression lines are depicted in red. The repeated line ratio increases over time for all five genres, indicating that lyrics are becoming more repetitive. This further substantiates previous findings that lyrics are increasingly becoming simpler11 and that more repetitive music is perceived as more fluent and may drive market success52. The strongest such increase can be observed for rap (slope \(m = 0.002516\)), whereas the weakest increase is displayed by country (\(m = 0.000640\)). The ratio of chorus to sections descriptor behaves similarly across different genres. The values for this descriptor have increased for all five genres. This implies that the structure of lyrics is shifting towards containing more choruses than in the past, in turn contributing to higher repetitiveness of lyrics. We see the strongest growth in the values of this descriptor for rap (\(m = 0.008703\)) and the weakest growth for R&B (\(m = 0.000325\)). The fact that the compression ratio descriptor (not shown in the figure) also shows an increase in all genres except R&B further substantiates the trend toward more repetitive lyrics. Another observation is that the lyrics seem to become more personal overall. The pronoun frequency is increasing for all genres except one (country with \(m = -0.000145\)). The strongest increase can be observed for rap (\(m = 0.000926\)), followed by pop (\(m = 0.000831\)), while rock (\(m = 0.000372\)) and R&B (\(m = 0.000369\)) show a moderate increase. Furthermore, our analysis shows that lyrics have become angrier across all genres, with rap showing the most profound increase in anger (\(m = 0.015996\)). Similarly, the amount of negative emotions conveyed also increases across all genres. Again rap shows the highest increase (\(m = 0.021701\)), followed by R&B (\(m = 0.018663\)), while country shows the lowest increase (\(m = 0.000606\)). At the same time, we witness a decrease in positive emotions for pop (\(m = -0.020041\)), rock (\(m = -0.012124\)), country (\(m = -0.021662\)), and R&B (\(m = -0.048552\)), while rap shows a moderate increase (\(m = 0.000129\)).
The second set of analyses first aims at investigating the interplay between lyrics descriptors, release year, and listening as well as lyrics view count. The employed multinomial logistic regression fits significantly better the data than the baseline model, i. e., a null model without predictors, indicating an increase in the explained variability (likelihood ratio chi-square of 314.56 with a \(p
To assess the effect of the predictors, the genre class rap (i. e., the one with the highest average lyrics view count), is considered as the reference class of the dependent variable. Our results show that the probability of a song being from country or rock instead of rap, according to its lyrics view count, varies across decades. As lyrics view count increases, the effect of the year slightly augments (in 1.07 odds) the probability of a song being from country instead of from rap: \(\beta (SE)=0.07(0.02)\), \(z=3.29\), \(p=.0009\). Differently, as lyrics view count increases, the effect of a raising year decreases (in 0.94 odds) the probability of a song being from rock instead of from rap: \(\beta (SE)=-0.05(0.01)\), \(z=-5.89\), \(p
Forest plot displaying the estimated multinomial logistic regression coefficients (standardized beta) for the prediction of musical genre. As reference class, rap i. e., the genre with the highest average lyrics view count, is considered.
Concerning emotion descriptors, the musical genre in which these play the most important role is rap, followed by R&B. For R&B the results show that the content of the lyrics becomes more negative with time, as shown by the increase in concepts related to anger and a detriment in positive emotions (cf. \(\beta = 1.75\) and \(\beta =-0.86\), respectively, in Table 4). Differently, for rap, there is a general increase in the use of emotion-related words with time, both negative and positive (cf. positive \(\beta\) for all the emotion descriptors), which indicates a tendency towards the use of more emotional words. Confirming outcomes from previous work13, as shown for R&B, also for pop and country, a tendency toward more negative lyrics is displayed over time; for rock, emotion seems to play a negligible role in the evolution of lyrics. As a final note, we would like to emphasize that since both the overall and block-wise adjusted R\(^{2}\) are very low, these results should be interpreted cautiously, and taken as tendencies rather than strong differences and could partly result from partly non-randomness in subsampling.
Both limitations, related to demographic bias and popularity bias, could be overcome by resorting to other data sources, notably the often-used Billboard Charts. However, using this data would introduce other distortions, among others, a highly US-centric view of the world, a much more limited sample size, and a lower granularity of the popularity figures (only ranks instead of play counts). In addition, Billboard Charts are only indicative of music consumption, not for lyrics viewing, which we particularly study in this paper. 0852c4b9a8
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