Ayres, Anna. "Billboard Hot 100 Lyrical Analysis." GitHub, 2023, https://github.com/spock0724/Billboard-Hot-100-Lyrical-Analysis/blob/main/BillboardHot100LyricalAnalysis.ipynb. Accessed 19 Jun., 2024.
In this public GitHub project, the author has scraped Wikipedia pages to access Billboard Hot 100’s yearly rankings from 1959 to 2023 and supplemented the data with lyrics using the genius API. The resulting downloadable CSV file would be very helpful for our project, as we could utilize the year and lyrical data to plot trends in the Hot 100 songs’ lyrics. It should be noted, though, that since the rankings were obtained from Wikipedia, which is unofficial and open to edits, they might be less credible or accurate. Additionally, the author produced several visualizations providing some insights into the rankings and lyrics. For example, the lyrical sentiment analysis by year shows a regression line of a mild decrease in the yearly average sentiment score, from slightly positive to even more neutral. However, the trend is not salient and the yearly averages might be too general to analyze. A mean sentiment score does not indicate whether most songs are neutral, or if there are extremely positive/negative songs that cancel each other out. Also, the author plotted the number of offensive lyrics using a list of radio words. Advancing from this idea, we could plot the trends of some common profanity words instead of a single summative trend, which would make the visualization more straightforward and meaningful to the audience.
Baillard, Matthieu. "Artist Info." GitHub, 2024,
https://github.com/matthieubaillard/gender-diversity-in-music-charts/blob/main/artist_info.csv. Accessed 19 Jun., 2024.
This dataset contains detailed information on artists, including their names and (socially inferred) genders, aiming to explore gender diversity in music charts. It was particularly useful for augmenting the "Billboard Hot 100 (1958-2024)" dataset by adding a gender column, enabling researchers to analyze trends in gender representation over time. A number of the gender entries in this dataset might just be socially inferred, meaning they are based on public perception and social conventions rather than self-identification, which could introduce biases.
One limitation observed is the potential inaccuracy in gender inference, as it might not accurately reflect the artists' true gender identities. Additionally, the dataset does not account for non-binary or genderqueer artists, limiting the scope of gender diversity analysis and application of queer theory. Despite these limitations, the dataset is a valuable resource for examining gender representation trends in the music industry over time. Available in CSV format, it can be integrated with other datasets like the Billboard Hot 100 dataset to provide a more comprehensive analysis of gender dynamics in popular music. This dataset supports detailed, nuanced investigations into how gender diversity has evolved on music charts, offering insights into the broader social and cultural shifts within the music industry.
Bloomberg. “This Is the Most Name-Dropped Brand in Music.” Fortune, 18 Aug. 2017, fortune.com/2017/08/18/name-brands-pop-music-rap/. Accessed 12 Jun., 2024.
This article shows a bar chart of the counts of the most mentioned brands in top-20 songs from 2014 to 2017 and an explanation of it. Although it is outdated and covers a short, not-so-recent time period , this was the inspiration for the branch of our topic where we analyze brand mentions in songs by count and apply Marxism, saying music is more of a product and money-maker than a message-spreader like it mainly was before. It shows how of the 212 brand name mentions in the 280 songs they mentioned were not just limited to big luxury brands, alcohol, or guns, as less glamorous brands such as Kit Kat and 7-Eleven have been mentioned in music, although most mentions consisted of car brands like Rolls-Royce and Ferrari. It also illustrates how brand name mentions are not just limited to rap and hip-hop songs by artists of color like how this trend started: white pop artists like Taylor Swift mentions Band-Aid bandages and Polaroid in two of her songs, while Meghan Trainor even mentions Adobe’s Photoshop in her hit “All About That Bass”. This part of the article inspired the branch of our project that examines intersectionality with artist identity in terms of race and gender.
Centers for Disease Control and Prevention, (2010, July 9),
Morbidity and mortality weekly report (MMWR), https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5926a1.htm
This article documents cigarette use among high school students from 1991 to 2009, showing an increasing percentage of active cigarette users peaking in the late 1990s and falling off afterwards. The CDC is a highly respected scientific institution and clearly lays out its methodology and data in the article, so its results are very likely to be reliable. In the context of this project, the observed peak in teenage cigarette use seems plausible as a consequence of the noted increase in cultural depictions of smoking around that time, as mentioned to exist in film and explored more fully with the music data visualizations, though of course the causation could be in either direction, or both could be caused by some unseen third factor. One of our hypotheses is that suppression of tobacco usage temporarily slowed or even reversed its decline by catching on with individualists and rebels, which would predictably be most visible among teens, as observed here. This would also be reflected in the corresponding subcultures and their music, which would lead to an uptick in casual reference to cigarettes, as observed in our primary Spotify and Billboard Hot 100 data sets.
Earhart, Elizabeth. "Billboard Hot 100 (1958-2024)." Kaggle, 2024,
https://www.kaggle.com/datasets/elizabethearhart/billboard-hot-1001958-2024/data. Accessed 19 Jun., 2024.
This dataset from Kaggle offers an extensive historical record of the Billboard Hot 100 charts from 1958 to 2024, detailing song titles, artists, peak positions, and chart durations on a weekly basis. Its granular nature allows for very precise analysis of trends in music popularity and the evolution of musical tastes over time. Unlike other datasets, this one captures weekly chart movements comprehensively, with no limitations on detail. However, it lacks a column for the gender of the artists, which is a limitation for studies focusing on gender representation in popular music over time. Researchers interested in such analyses would need to augment the dataset with additional gender-related data. The dataset is available in CSV format. It also lacks lyrics of the songs in each entry, so a lyrical analysis of this dataset would not be possible unless lyrics are also augmented. However, this dataset is regularly updated to include the latest chart information, making it a reliable tool for academic research and personal interest. This dataset is invaluable for researchers, data scientists, and music enthusiasts aiming to understand the dynamics of the music industry and the factors influencing chart success. The detailed, weekly data entries support robust and nuanced analyses of the Billboard Hot 100's history, providing insights into the shifting dynamics of music trends and industry impacts over several decades.
Hesmondhalgh, David. The Cultural Industries. London: SAGE Publications, 2013.
David Hesmondhalgh's book provides a comprehensive analysis of the cultural industries, including music, through a Marxist perspective. He looks at how cultural products are produced, distributed, and consumed within capitalist economies. He highlights the power dynamics and commodification processes which are inherent in these industries. This resource can help us understand and explain the analysis of our data on music, seeing it as a product for money-making instead of more pure intentions like spreading a message or true entertainment. For example, Hesmondhalgh’s examination of commodification can help understand how gender and race affects artists' popularity. There this resource can be used as an explanatory ‘backend’ for our results from data. Furthermore, the author sheds light on the power dynamics in the music industry which can show any barriers and limitations that some racial and gender groups face due to the system. Moreover, since the source focuses on the industry from an economical point of view, this resource aligns well with our brand name analysis. Finally, the book provides real-world and case-studies which we can use as an inspiration or example in our own project. The main limitations are the broad range of topics and the age of the book. The author doesn’t focus solely on the music industry and so not all insights are applicable to our project. The book was published over 10 years ago. Music industry is always evolving, so the book might use outdated or inaccurate data.
Kibria, R. (2017, June 25). Singers’ gender. Kaggle. http://www.kaggle.com/datasets/rkibria/singersgender
This dataset categorizes singers by gender, scraped from Wikipedia categories, and includes three columns: artist, gender, and category. The data was obtained through web scraping and concatenated using Python's pandas library, then saved to a CSV file. Data captures detailed information about singers, including their names, gender, and associated categories. The file contains 23,177 unique values, with male singers making up 66% and female singers 34% of the dataset. This structure provides a solid foundation for analyzing gender representation in music, particularly in relation to the Billboard Hot-100 dataset. This dataset is particularly useful for our interest in lyrical analysis and gender studies. By integrating this dataset with the Billboard Hot-100 dataset, we can explore how gender influences lyrical themes and representation in popular music. This integration enables us to perform topic modeling, and other NLP techniques to understand the differences in lyrical content between male and female artists. This can reveal insights into how gender roles and stereotypes are reflected and perpetuated in popular music. However, the dataset relies on Wikipedia categories, which may not be exhaustive or entirely accurate. Some artists may be misclassified or missing from the dataset. Additionally, the dataset does not specify the frequency of updates, which means it may not reflect the most current information. These limitations should be taken into account when conducting our analysis and interpreting the results.
Laing, Dave. "The Music Industry and the ‘Crisis’ of ‘Digital Labor’: A Marxian Analysis."
Media International Australia 152, no. (2014): 156-165
Dave Laing's article explores the concept of 'digital labor' in the music industry using a Marxian lens. He makes the argument that digital platforms like Spotify and YouTube have reshaped labor practices, leading to the exploitation and devaluation of musicians' work in the process. Laing also discusses how these streaming services dominate the market, getting most of the control and profit, which ultimately leads to the exploitation and alienation of musicians. He also discusses how musicians are often required to engage in unpaid or underpaid labor, such as self-promotion and social media engagement, which mirrors Marx's concept of surplus value where the labors of musicians generate significant profits for platform owners, and as a result, certain creators receive a small share of the profit. He then discusses new alternative forms of music production and distribution that aim to create more equitable working conditions for digital laborers. This article is a particularly helpful source for researchers seeking to further understand how commodification affects and has affected creativity and content production. It also serves as a good example of how Marxist concepts can be applied to music and the industry, providing valuable insights for digital humanities projects examining similar dynamics. The biggest limitation of the article is its focus on the theoretical aspects with less attention given to empirical data. Therefore, it can only serve as a supplement to our data analysis.
Nakhaee, Muhammad. "Audio Features and Lyrics of Spotify Songs." Kaggle, 2022, https://www.kaggle.com/datasets/imuhammad/audio-features-and-lyrics-of-spotify-songs.
Accessed 19 Jun., 2024.
This dataset contains various information including audio features (loudness, speechiness, acousticness, instrumentalness, liveliness, valence, tempo, etc.) as well as music genre on 18,454 Spotify songs from 1958 to 2020. The original tidytuesday project dataset accessed Spotify songs using the spotifyr pacakage, and this dataset is an augmentation to it with lyrics information accessed using the genius library in R. As the uploader cautions, only about half of the original songs are available in this dataset, as they could not retrieve the lyrics for many songs. This is expected as there was a broad spectrum of categories of songs (e.g. EDM) that lack lyrics from the original dataset, which should not affect the quality of our lyrical analysis much. This dataset is powerful as it covers a long time period, but we recognize that the sheer contrast between the number of songs from pre-1970s (less than 50 songs per year) and that of songs from more recent time (over 3,500 songs from 2020) might affect over-time analysis on the frequency of words. Additionally, the dataset’s columns on audio features and genres can be helpful for professionals interested in analyzing modern music’s characteristics from the perspective of music theories.
Parris, Daniel. “The Rise of Explicit Music: A Statistical Analysis.” Stat Significant, 29 June 2023, www.statsignificant.com/p/the-rise-of-explicit-music-a-statistical. Accessed 20 Jun., 2024.
This article discusses the rise in profanity in music with a statistical analysis. It first gives a brief context of why explicit music was initially a major issue for society, or at least parents, with an example of Tipper Gore, former U.S. President Al Gore’s wife, and her actions to “ban” explicit music. Then, it details why this rise in frequency of explicit music in the Billboard charts happened from the 1990s to 2021, attributing this to the rising popularity of music streaming that increased accessibility as a loophole for younger audiences. Then, they show a few graphs (which we neither use nor reference since their dataset is different) that show this rise, with the percentage of non-explicit versus explicit songs on the Billboard Charts from 1985 to 2020, the same percentages by genre, and artists with the most explicit Billboard songs. Each has some explanations of the statistics with examples, even looking at attributes like danceability and positivity for sentimental analysis. For our project, this article gives context to the rise in Billboard songs with profanity during the mid-2010’s, corresponding to the “Proportion of Billboard Hot 100 Songs Mentioning (Profane Words) vs. Year” line graph (created by us).
Rose, Christine Ann , Sweanor, David T. , Hilton, Matthew J. and Henningfield, Jack. (2024, May 19)
"The age of the cigarette". Encyclopedia Britannica,
https://www.britannica.com/topic/smoking-tobacco/The-age-of-the-cigarette
Accessed 20 Jun. 2024.
When exploring the data in preparation to write the section of the narrative involving substance words, the word “smoke” jumped out as having an unusual spike. This was then linked to depictions of tobacco, specifically, and it became apparent that a source regarding this and the broader background of popular tobacco use was necessary. This article by the Encyclopedia Britannica documents the history of cigarettes. It focuses on popularity of consumption and the attitudes towards tobacco that affected popularity, covering early usage in the late 19th century through popularization, revelation of health risks, and decline in the modern day. As an article of an encyclopedia, it is a tertiary source, lacking many details and picking the results of more direct works as examples rather than tabulating any data, but it provides useful context for the section of this project that involves smoking, and mentions the jump in depictions of smoking noted therein. We chose to use the article concerned with cigarettes because they are the most common form of tobacco consumption in both reality and popular depiction, and are commonly referred to as “smokes”, which implies a high likelihood of them being the relevant factor in the phenomenon we found.
Rune. “Billboard Hot-100[2000-2023] Data with Features.” Kaggle, 9 June 2024,
www.kaggle.com/datasets/suparnabiswas/billboard-hot-1002000-2023-data-with-features
For our project on the intersectionality of race and gender in music, the Billboard Hot-100 [2000-2023] dataset is a promising resource. This dataset compiles Billboard Hot-100 songs chart data from 2000 to 2023 and enriches it with lyrics from Genius and audio features from Spotify. The dataset includes extensive metadata for each song, such as its rank, title, artist, year, lyrics, and various Spotify audio features like danceability. The dataset is well-organized in a CSV file containing 26 columns. This detailed structure facilitates deep exploratory data analysis and advanced visualizations, making the dataset highly applicable for our project. The dataset is particularly valuable for our interest in lyrical analysis, such as examining differences in lyrical themes across various groups. We can leverage the lyrics data to perform sentiment analysis, topic modeling, and other NLP techniques to explore how different genres, artists, and time periods have influenced lyrical content. This can reveal insights into cultural and social trends reflected in popular music lyrics. However, there are potential limitations to consider. The dataset focuses on songs that made it to the Billboard Hot-100, which means it may not fully represent the diversity of musical genres and artists, especially those from marginalized groups who may not have achieved mainstream success. Additionally, the lyrical data from Genius relies on user-contributed content, which can sometimes be incomplete or inaccurate. Also the data lacks a clear category showing race, ethnicity or gender of the artist. The Spotify audio features, while useful, are limited to quantifiable attributes and may not capture the full emotional or cultural context of the songs.
Tamplin, K. ``Meet the Trailblazing Female Vocalists of the 1960s.” Ken Tamplin Vocal Academy,
12 Feb. 2024, kentamplinvocalacademy.com/singer-types/decade/female-60s/. Accessed 20 Jun. 2024.
Ken Tamplin gives an overview of how female singers changed the music scene in the 1960s. He starts by talking about the rise of different music genres like rock, pop, and soul during this time. The article highlights how female singers broke traditional gender roles and made a lasting impact on the music industry. Key figures like Aretha Franklin, Diana Ross, and Dusty Springfield are noted for their unique contributions and famous performances. He mentions other important artists like Janis Joplin, Etta James, and Tina Turner, along with rising stars like Marianne Faithfull, Lulu, and Petula Clark.
In this article, he also explores the different music styles these women embraced and their influence on the music industry and discusses the social and cultural significance of their work. Iconic songs, albums, and memorable performances from these artists are highlighted for their ongoing impact. The article concludes by recognizing the lasting influence of these singers on later generations, celebrating their role in shaping both the culture of the 1960s and the music industry as a whole.
Relation to Intersectionality and Identity in Female Artists: Tamplin highlights the challenges faced by female singers in the 1960s, especially in dealing with both gender and racial issues in a male-dominated industry. Aretha Franklin's powerful representation of both race and gender, Diana Ross's leadership in The Supremes as a Black woman, and Dusty Springfield's blend of pop and soul show the complex identities these artists navigated. Their success in breaking down barriers and exceeding expectations reflects the interplay of identity and artistic expression during this time. These stories are important for understanding how female singers not only shaped the music industry but also contributed to social and cultural changes in the 1960s.
Topaz, Chad M et al. “Race- and gender-based under-representation of creative contributors: art, fashion, film, and
music.” Humanities & social sciences communications vol. 9,1 (2022):
221. doi:10.1057/s41599-022-01239-9 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9244133/ Accessed 20 Jun. 2024.
This empirical study examines the gender and racial/ethnic composition of influential figures in four creative fields: contemporary art, high fashion, box office film, and popular music. The study highlights significant underrepresentation of women and marginalized racial/ethnic groups in these domains despite their substantial presence in the general U.S. population. Women, constituting 51% of the population, represent only 28% in contemporary art, 45% in fashion, 27% in film, and 17% in popular music. Marginalized racial/ethnic groups, making up 39% of the population, are also underrepresented, with notable exceptions in music where Black artists constitute 48%. However, higher representation does not necessarily indicate equity or inclusion. White men are consistently overrepresented across all fields. The study underscores the severe exclusion of marginalized identities, including women and various racial/ethnic groups, and the challenge of inadequate demographic data.
Relevance to Identity and Intersectionality in Music Industry Success:
This article is crucial for understanding the dynamics of identity and intersectionality within the music industry and other creative fields. It provides a comprehensive analysis of how gender and racial/ethnic identities intersect to influence representation and success in the music industry. By highlighting the disparities and overrepresentation of certain groups, the study sheds light on the systemic barriers faced by women and marginalized racial/ethnic groups. The findings about the music industry, where Black artists have higher visibility yet still face inequity, illustrate the complex relationship between representation and genuine inclusion. The study’s emphasis on the need for better demographic data and the recognition of diverse gender identities further enhances its relevance to discussions on diversity and intersectionality in the creative arts.