Was it easy to guess the gender identity of the singer in those breakup songs? If it was, that's not surprising: gender runs deep in how we express ourselves, and pop music is a place where roles are often highlighted and reinforced.
Pop music is the soundtrack and backdrop to our lives. The lyrics influence our views on gender and relationships for better and frequently for worse. They allow us to express ourselves at key moments, such as when love goes bad. Yet many song lyrics have themes that are often based on stereotypical gendered roles.
In the US music industry, the number of men outnumbers the number of women, especially in roles of power such as songwriters and producers. According to a recent report on diversity among music creators from the University of Southern California's Annenberg Institute, about 21 percent of artists are women, as are 12 percent of songwriters and about 2 percent of producers. Unfortunately, there is not enough data collected on non-binary artists in the music industry and the role they play within the sector.
Not only are women and other gender identities in power largely absent within the music industry, but there is also an imbalance of who we hear on the radio and streaming platforms. According to aSpotify report released in 2020, only about one in five streams are by female artists. An even smaller number of non-binary artists are being streamed. The imbalance in gender and power in music influences all aspects of the industry, down to the songs that are performed by and written for artists.
Gender representation in lyrics highlights a larger issue of gender representation in the music industry. Regardless of the gender of the artist, the majority of the producing and songwriting teams are still men. Due to the majority of producing teams are most men, many songs have similar structures and overall theming of these songs. However, driving deeper into these songs there are clear differences in how songs are written and framed.
Most of these differences are based on gendered stereotypes, such as negative views on one's ex and how the singer views.Many of the themes in these songs have heavy gendered stereotypes, espically songs written by male artists. These themes are present thoughout the majority of the pop music that we have listen to and what is on the radio.
Even in songs with similar structures, the lyrics are usually heavily gendered. Many pop songs' lyrics reinforce tropes and gendered stereotypes, especially when talking about love and exes.
On the surface, many of the songs that were discussing love and breakups follow similar lyrical themes and structure. Regardless of gender, most focus on the major themes of anger and emotions as well as negative feelings towards their exes. Both male and female singers tend to focus on three things: the singers themselves, the ex, and the relationship. Lyrics focusing on themselves include the singer's feelings or actions. Lyrics about their exes focus on what the ex has done since the breakup or how they might be feeling. Lastly, lyrics about the effects of the relationship usually focus on how it changed the singer.
While there are surface-level similarities there are important differences once the lyrics are analyzed and these themes are looked further into.
Based on the sample of 64 breakup songs from the Billboard Top 100, regardless of gender artists tend to use about the same amount of 'I' and 'you' pronouns in songs. However, songs by female identiting artists uses slightly more 'our' and 'us' pronouns.
The lyrics in the male artists' songs focus more on anger and they would often go party or drink after the break-up.
The lyrics in the female artists' songs tend to be more focused on themselves and how they're feeling.
While there are two extremes with song lyrics between male and female artists, there are a songs that were not as explicit in these views. For example, songs themes mostly focus on the singer's emotions and how they felt after the break up but did not include another major theme such as self improvement or partying/sex/drugs etc. Thus making these songs themes more netural compared to songs that show more explicit themes.
Even in songs with similar structures, the lyrics are usually heavily gendered. Many pop songs' lyrics reinforce tropes and gendered stereotypes, especially when talking about love and exes.
"Don't," Ed Sheeran
"Hot Girl Bummer," Blackbear
Male artists generally focus on what they are going to do after the breakup: party, drink or do drugs, or have sex with someone new. Male singers tend to be angry towards their ex, call them names and blame them for the breakup.
"Lose You to Love Me," Selena Gomez
"New Rules," Dua Lipa
Female artists generally focus on their emotions, and thoughts about the relationship and the aftermath of the breakup. These stereotypes reinforced the notion that women are more "emotional” , with the lack of gender representation in music these songs may be channeling the views of a male songwriter. With the low number of women and non-binary song writers, songs sung by non-male artist trend to have a male song writting team, which can influence how music sounds.
"Apologize," One Republic
"We Are Never Ever
Getting Back Together,"
Taylor Swift
However, there are still overlapping themes of emotions caused by the breakup and what happened afterward. In addition, not all songs were as explicit with these themes and there were songs that were in the middle of the spectrum. The main shared themes were anger and regret in the aftermath of the breakup. The main difference was what language was used and how the artists talk about the breakup or their ex.
The lack of women songwriters and producers creates an unhealthy culture for those in the industry but the imbalance in representation also leads to song lyrics having gendered themes and perpetuating stereotypes.
Regardless of the artist's gender identity, the majority of their songwriters and producers are men, often many of these teams are made of all men or a small handful of women. However, even with a majority of the songwriters and producers being males, there are differences in how songs are written depending on the artist that was singing the song. Such as that songs sung by women were more "emotional" and songs sung by men were more "angry". However, these stereotypes and themes can be due to the lack of non-male folks in power, causing for the lack of representation in the lyrics and harmful lyrics.
The lack of gender representation in roles of power direcetly influences how music is written and maketed towards listeners. In addition, with the lack of gender represenation in roles of power it allows for music to have negative stereotypes and views on certain groups such as women and non-binary folks. Music plays an important role in how people view others and how they express themselves.
It is important to understand the how power and gender influence the music industry, the inequality impacts how music is produced and influences the messaging in the pop songs.
However, there are some early signs of a shift in the industry, with more queer artists producing and writing music. The increased representation of queer and non-binary artists may allow for a shift in the music industry in the future to be more inclusive.
In addition to the increase of queer artists in music, there has been an increase of women Grammy nominees recently. In 2020 the category of "Song of the Year" had 44% women nominees, the highest number of nominees in recent history.
With the slow change in the music industry with gender representation, there might be a shift in culture within the industry and a possible change in how lyrics and songs are written.
To analyze gender pronouns and themes in the songs, lyric data was collected online and then entered into AntConc and Voyent Tools, allowing for lyrical analysis such as word combinations and collecting usage of pronouns. While R and Flourish were used to create the data visualizations of the pronouns, I used Julia Silge's R code Gender Roles with Text Mining and N-grams to help create visualizations. In addition to the R code, Flourish was used to make some other graphics.
The corpus of the songs included 64 pop songs from the Billboard 100, 30 songs were from male artists, 30 from female artists, and 4 from non-binary or artists that use they/them pronouns in addition to she/her or he/him pronouns. The songs were picked from using the Kaggle Dataset that had Billboard top 100 songs since 1958, songs were picked from the past three decades and what was considered “breakup” songs.”
To collect the number of pronouns in each of the songs, I manually picked breakup songs from Billboard and collected the lyrics into a spreadsheet. Once those lyrics were saved I imported the files into AntConc and scraped through the list of break-up song lyrics. In AntConc, I used the advanced search option to search for the pronouns: "I", "You", "me", "he", "she", "we" and "us". Once the lyrics included those words, I saved them and cleaned the data in a spreadsheet. Data clean up includes cleaning up contractions and correcting spellings of words.
After the words were collected and saved, the data was imported into R, and using the code from Julia Silgle's GitHub I was able to create some data visualizations using the pronouns and highlighting what words came after them. In addition, the data set was imported in Flourish and I created the visualizations of the pronouns, allowing for visualizations to be created based on the number of pronouns that appear.
To count for themes of the songs, I manually scrapped lyric data from the songs and put the major themes into four different categories: anger, self-improvement, sadness, and partying/drinking.
Artists', songwriters', and producers' gender identities were collected from their social media pages, Wikipedia page and online articles and news sources. Song data come from lyric sites such as Genius and AZLyrics, and songs that were chosen came from the Billboard 100's charts. The songs that were picked had lyrics and themes that had some sort of references to a past relationship or an ex.
Produced by students of the Media Innovation masters' program at the Northeastern University School of Journalism. © 2021