Introduction

In this report, we analyze a dataset derived from Rolling Stone Magazine, a renowned publication that covers music and pop culture. Since 2003, the magazine has published its “500 Greatest Albums of All Time” list approximately every nine years, sparking widespread discussion and debate. As with many music rankings, these lists have been met with mixed reactions from audiences, reflecting the deeply subjective nature of musical taste. In fact, because some consumers disagree with the album rankings and accuse the publication of having biases in their rankings. To explore the accusations, we will evaluates which variables most effectively predict how quickly an album achieves Top 500 status. Additionally, we will investigate if rankings are biased toward certain genres or certain artist demographics. Lastly, we will analyze the most common words found among the album titles and the typical length of album titles to evaluate if album name can impact its popularity. Through this analysis, we hope to learn more about the musical industry and uncover any systemic biases that may shape the music industry’s recognition of artists. These findings are highly beneficial not only for the creation and advertisement process of aspiring artists but also to the listening decision process for the audience. We aim to provide deeper insights into the factors that drive music’s cultural impact, as well as uncover hidden patterns and biases within this iconic ranking system.

Data Description

The Rolling Stone Ranking dataset was found on github in the Tidytuesday forum. The data set contains information for each album that has made the Rolling Stone’s “500 Greatest Albums of All Time” lists in 2003, 2012, and / or 2020. Each row corresponds to one album. There 692 rows. The column variables are …

  1. clean_name (Cleaned name of the artist / group)
  2. album (album name)
  3. rank_2003, rank_2012, and rank_2020 (Rank of the album in the respective years. NA if album not released yet or not in top 500)
  4. differential (2020-2003 ranking differential. Negative if it went down in the chart and positive if it went up)
  5. release_year (Release Year)
  6. genre (Album Genre. NA if uncategorized)
  7. weeks_on_billboard (Weeks on Billboard)
  8. peak_billboard_position (Peak Billboard Position)
  9. spotify_popularity (Spotify Popularity on a 1-100 scale based on commercial success. NA if not on Spotify)
  10. spotify_url (Spotify URL. NA if not on Spotify)
  11. artist_member_count (Number of artists in the group)
  12. artist_gender (Gender of the artist(s). Male/Female if it’s a mized group)
  13. artist_birth_year_sum (Sum of the artists birth year)
  14. debut_album_release_year (Debut Album Release Year)
  15. ave_age_at_top_500 (Average age at top 500 Album)
  16. years_between (Years Between Debut and Top 500 Album)
  17. album_id (Album ID. NOS at the beginning of the ID if not on Spotify).

Research Questions

  1. What variables are good predictors of how quickly an album is able to achieve a Top 500 ranking?
  2. Are there gender, age, and genre biases in album ranking and Spotify popularity?
  3. Among albums that have been placed on the Rolling Stone 500 list, what words are the most common among the titles and what is the typical word count? Furthermore, are albums that include at least one of the 5 most common words more popular than those that don’t?

Research Question 1

The speed at which an album achieves a Top 500 ranking is a point of interest within the music industry, especially for artists seeking to improve the visibility of their work. It is likely to be influenced by a variety of factors, such as initial chart performance to the artist’s personal popularity. This analysis explores the potential variables that contribute to an album’s rise on the Rolling Stone Top 500 list. By examining the correlation between the response variable (years between debut and top 500 album) and other variables in the data set, we aim to identify the predictors that enable an album to secure its place among the greatest artists of all time in the shortest amount of time!

We would like to preliminary examine the distribution of our variable of interest (response variable), years between debut and getting ranked into the Rolling Stone Top 500.

We see that the majority of top 500 ranked artists achieved such status in under 10 years from their time of debut. As the years since debut increases, the number of top 500 ranks dramatically drops starting around the year 5 mark.

From here, we would like to understand the nature of the relationship, if any, between the relevant quantitative predictor variables [Spotify popularity, weeks on billboard, peak billboard position, average age of artist (or artists for groups) when their top 500 album was produced, and number of members] and years between debut and top 500 ranking.

From the pairs plot, we see a strong positive linear relationship (r = 0.880) for the variables years between debut and Top 500 album and average age of the artist(s) when their album made the Rolling Stone Top 500 list. We also see a weaker positive correlation between the peak Billboard position and years between debut and Top 500 album. Lastly, we note weak negative correlations between average age of the artist(s) when their album made the Rolling Stone Top 500 list and the variables Spotify popularity, number of members, debut album release year.

Contextually, this means that as average age and peak Billboard position increases, the expected time between getting ranked into the Rolling Stone top 500 increases as well. These are both valid patterns logically because as an artist gets older, their relevance within the current music market begins diminishing possibly due to lower understanding of or ability to relate to the younger generations. Same with Billboard rankings since lower ranked (represented by higher raw rank numbers i.e. how rank 100 is worse than rank 10) albums on an extremely popular music platform such as Billboard are likely to take longer to gain universal recognition, perhaps due to lower visibility or simply objectively lower quality, and earn a spot on the Rolling Stone’s top 500.

On the flip side, as number of members, Spotify Popularity, and debut album release year increases, the time between getting ranked into the Rolling Stone top 500 decreases. For Spotify Popularity, it is on a 0-100 commercial success scale such that lower raw success scores indicate higher income generated on the album (i.e. how score 100 means better commercial success than score 10) - albums that generate more income are those that have been listened to the most often and have likely been digested positively, which should scale onto how fast their Rolling Stone Top 500 ranking is earned. The same goes number of members where having more members for people to fan over, spread marketing via more individual member accounts/agencies, and/or distribute the workload across are all possible ways to cut down the time it takes an album to get into the Rolling Stone top 500 rankings. The negative correlation between the time getting ranked and debut album release year makes us speculate that the Rolling Stone may have a preference towards recent artists. Though, we merely speculate on the relationships between the response variable and its predictors as we lack evidence to conclude causality.

Next, we build multiple linear regression model. We chose two variables with the strongest correlation with years_between to include in our model.

## 
## Call:
## lm(formula = years_between ~ ave_age_at_top_500 + debut_album_release_year, 
##     data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -24.9958  -1.8128  -0.0153   1.9202  12.6382 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              63.36821   21.08455   3.005 0.002749 ** 
## ave_age_at_top_500        0.77362    0.01694  45.665  < 2e-16 ***
## debut_album_release_year -0.04064    0.01059  -3.839 0.000135 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.96 on 683 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.7795, Adjusted R-squared:  0.7789 
## F-statistic:  1207 on 2 and 683 DF,  p-value: < 2.2e-16

We further supplement our previous observations with a multiple linear regression analysis. From the R^2 value, we can conclude that 77.89% of the variation in years between debut and top 500 ranking is explained by our 2 predictors which suggests that a multiple linear model is a moderately strong fit to this data and of sufficient statistical significance too due to the p-value < 2.2e^(-16) < (.05 = alpha).

Individually, we see that both beta values are significant. The average age at introduction to the top 500 is a positive relater, meaning that artist age was a very telling predictor of how long it would take for their album to get ranked into Rolling Stone’s top 500 and that older meant longer. Furthermore, debut album release year is a negative relater. This means -0.04 years is the expected difference in years to get ranked into Rolling Stone’s top 500 between two artists who happen to differ in debut album release year by 1.

As for a key categorical variable, genre, we will investigate the similarity of each genre based on long it takes for albums under each to enter the top 500 list.

As a reminder, our similarity metric is pairwise distance based on the years_between response variable. From here, we see that Afrobeat is most dissimilar from all the other genres due to having the largest branch length away and also not being under the same immediate bracket with any singular genre. On the other hand, we see that Electronic and Flunk/Disco are the most similar.

Research Question 2

In the world of music, recognition often comes with both cultural and historical significance as a testament to the hard work dedicated. The Rolling Stone Top 500 list has long been a benchmark for musical excellence. However, as with many long-standing traditions, it’s crucial to explore whether the rankings are skewed by gender and age. In this section, we explore how these factors might influence an album’s placement on the most recent (2020) Rolling Stone Top 500 list. By analyzing the gender and age of the artists behind these iconic albums, we strive to uncover potential patterns that could reveal underlying biases in the ranking process and offer insights into the broader dynamics at play in the song making industry. We will also tie these biases into potential preference biases towards some genre(s) over others.

From the regression analysis performed previously, we saw that age had a positive relationship with years between an album’s debut and getting on the Rolling Stone top 500 list. Now, we want to see if there are certain trends within age, specifically between age groups, that affect its order within the top 500 list.

The key findings are that largest concentration of artists in the top 500 are in the <30 age group. This could indicate that older artists are at a disadvantage in terms of rank order, reaffirming our linear regression conclusions. We see that the <30 group is able to achieve the best ranks throughout its entire box whereas 50+ achieves roughly the lowest ranks through its entire box so there does appear to be age bias within the top 500 list. The median rank is also lowest for the <30 list indicating a that younger artists generally rank higher. This alsoimplies there may be some biases against more seasoned artists in achieving this level of mainstream recognition.

We are also curious in the gender bias, if any, within the top 500 ranking system. However, to tie this into real-world impact such as commercial success as measured by Spotify popularity, we want to first confirm that higher Spotify popularity leads to lower and thus better ranking within the top 500 list.

## 
## Call:
## lm(formula = rank_2020 ~ spotify_popularity, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -293.469 -119.152   -6.524  121.127  305.627 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        376.6114    26.4246  14.252   <2e-16 ***
## spotify_popularity  -2.1981     0.4435  -4.956    1e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 141.1 on 476 degrees of freedom
##   (213 observations deleted due to missingness)
## Multiple R-squared:  0.04907,    Adjusted R-squared:  0.04707 
## F-statistic: 24.56 on 1 and 476 DF,  p-value: 1.001e-06

With the spotify_popularity coefficient having a coefficient of -2.1981 in the linear model, we see that an album that happens to differ in Spotify popularity to another album by one unit are expected to rank 2.20 places higher.

From this scatterplot, we see that overall the field is clearly very male-dominated as the graph is majority populated by red points. Both both genders are at the lower end of the average age spectrum when their album enters the top 500 ranking. There is roughly even spread across Spotify popularity for younger ages for both genders, though the higher average ages at which artists get ranked into the top 500 list appear to be for male artists whereas the female artists within the top 500 stick towards the younger side. This shows how the older end of the top 500 album artists are generally male, while female artists that make the list are generally young.

To concentrate data patterns, we selected just the top 100 out of the top 500 list from the most current year of 2020 to analyze systematic gender bias and genre preferences related to how the top 100 albums were ranked. From this stacked bar graph, we confirm that this is indeed a male-dominated industry or at least list. Additionally, the majority of the albums in the top 100 belonged to the hip-hop/rap genre with the least belonging to rock n’ roll/rhythm and blues genre, indicating shifts in overall music taste towards more modern creations from classic styles to more contemporary ones. These separate music styles (genres) are also reflective of time/age since rock n’ roll/rhythm blues was a primary development of the 1940s-1950s in the U.S. whereas hip hop/rap is dated more around the late 1970s and remains extremely popular in present data (even beyond 2020 into 2024).

Overall, we can see that the most current top 500 (and 100) ranking leans towards more modern and lovely music genres, male artists, and younger artists with successful (based on ranking within the top 500) older artists being almost entirely male. Age and especially gender biases are evident, as are music taste trends.

Research Question 3

In this section we will be evaluating what words are the most common among albums that have been ranked in the top 500, and what the typical word count is. Furthermore, we will determine if including common words in album names affect the popularity of an album. The overall goal of this section is to identify the affect between album name composition and its potential affect on album ranking and popularity.

To identify commonly used words in album titles that have made the Rolling Stone list, we created a word cloud showcasing the top 60 most frequent words from the album names in the data set. To ensure consistency, we cleaned each title by applying lowercase, removing punctuation, and eliminating extra whites space. Furthermore, we removed stop words (ie. “a”, “the” .”is”) from consideration and applied stemming so words with the same root were counted together (ie. words like “hit” and “hits” would be considered the same word). The word cloud is displayed below.

##     love      hit    black greatest     live 
##       15        9        8        7        7

We see that the 5 most common words are “love”, “hit”, “greatest”, “black”, and “blue”. The most common word being “love” is unsurprising and suggests a recurring theme of romance in album naming. In addition, colors such as “blue” and “black” appear often and may hint at common imagery or symbolism in album titles on the list.

Next, we are interested in whether including one of the 5 most common words in the album name will impact its popularity. To investigate this question, we created subsets of the dataset for albums that contained at least one of the most common words and albums that did not, and compared the distribution of peak billboard position (a measure of album popularity) for the two subsets.

The distribution of both two groups is skewed-left, however there are differences in their peaks. For albums without common words in their names, there is a high peak towards the top of billboard position and a smaller peak near the bottom of peak billboard position. This result suggests that albums that don’t contain common words in their titles typically ranking towards the top or the bottom of billboards, with few ranking in the middle. For albums with common words in their names, there is also a strong concentration towards higher rankings but the density peak is smaller than that of the other group. Furthermore, the right tail of the distribution is more uniform. This suggests that albums that contain common words in their titles typically perform well in terms of peak chart performance, but when they don’t peak billboard position is equally likely to be in the middle or towards the bottom. In addition, the median peak position for album using common words is slightly higher than for albums with common words, which suggests slightly worse performance for albums in this group. Based on the density graphs, we cannot conclude that including common words in album names directlyimproves popularity.

Besides investigating common words and their potential affects, we are also interested in the typical length of a title from the dataset.

We observe that the majority of album titles (around 70%) fall in the 1-3 word range, indicating that shorter titles are the most prevalent among the dataset. About 25% of album titles have moderate length, showing smaller but still notable proportion. Lastly, very few albums have titles with 7 or more words, with proportions dropping sharply as the word count increases. We cannot conclude if this distribution is only true for albums that have made the top 500 list, or albums in general. However, it is unsurprising that short titles are the most prevalent as concise titles likely easier to recall and market to consumers.

Main Conclusions

In this analysis of albums featured in Rolling Stone’s “500 Greatest Albums of All Time,” we explored the relationships between various factors and their potential impact on album rankings and popularity. Listed below are our main findings.

Finding 1: The majority of top 500 ranked artists achieved that status in under 10 years from their time of debut. Furthermore, the average age of the artist(s) when they made their top 500 album and the debut album release year of an artist are good quantitative predictors of how long it takes an artist to be ranked on the top 500 list. Lastly, genre also seems to impact how fast an artist becomes ranked, with albums in the Afrobeat genre taking the longest.

Finding 2: There is evidence of gender and age bias in the Rolling Stone rankings. The albums on the list are biased toward males and younger individuals, with patterns that suggest biases are persistent in the music industry as a whole.

Finding 3: There trends among the albums that have made the top 500 list. Among album titles, the five most common words are “love”, “hit”, “greatest”, “blue”, and black, while the typical length of a title is between 1 to 3 words. There is also no evidence showing that albums that include common words perform better in compared to those that don’t.

Future Work

While the project has addressed key research questions related to genre characteristics and their predictive variables, there are additional questions that remain unanswered and could be explored in future work. One particularly promising avenue is analyzing trends across time to gain a deeper understanding of how variables like Spotify popularity, genre rankings, and album recognition evolve over the years.

For example, a time series analysis could reveal whether modern genres like Hip-Hop/Rap and Electronic are increasing in popularity more rapidly compared to traditional genres like Jazz or Blues. Similarly, trends in how quickly albums achieve a Top 500 ranking could provide insights into whether the music industry is becoming more digitally driven over time. However, the dataset we currently have does not include enough years of data to perform a robust time series analysis. To effectively model and analyze such trends, we would require data spanning multiple decades or more granular year-by-year data on album rankings and Spotify popularity.

Future work could focus on collecting additional historical data or collaborating with platforms that have access to a broader temporal range of music popularity metrics. With such data, we could apply advanced statistical techniques like machine learning-based forecasting methods to uncover these time-dependent patterns. This approach would provide a richer and more nuanced understanding of the evolution of music genres and their relationship with digital metrics and cultural trends.