Tuesday, May 30, 2017
Condensed version of the Jay Z Dataset presentation
By Kenton Rambsy and Howard Rambsy II
[Note: this is a condensed version of our presentation at the Cultural Analytics symposium.]
What if a dataset on the rap artist Jay Z served as a blueprint for DH projects on African American literature? We arrived at that question after collecting and organizing a broad body of information on this one rapper. Today, we want to briefly describe some of the main components of our Jay Z Dataset and explain how our project relates to the symposium theme – cultural analytics.
One of the most crucial imperatives among scholars of African American literature over the last 20 to 30 years has involved showing the relationship between black writers and black writers. That is, scholars have sought to highlight the ways that black writers signify on or allude to the works of other black writers. That practice is known in some areas as intertextuality. And really, it’s that spirit of interconnectivity that has guided our research and writing on Jay Z. We began by trying to highlight connections between “formal” or conventional African American writer artists like Frederick Douglass, Toni Morrison, and others, and rap artists like Jay Z, Nas, and Outkast. At the same time, we were thinking about digital humanities, and what we discovered early on was that a figure like Jay Z presented us with data collecting opportunities in ways that those formal literary artists did not.
We assembled our Jay Z dataset by drawing on a variety of sources, including Rap Genius, Who Sampled, Billboard, and Wikipedia, and we used Voyant text mining software to extract numerical data from Jay Z’s lyrics across 12 studio albums. Accessibility to those sources allows us to draw on disparate sets of information in order to make discoveries about Jay Z. In short, our dataset combines thematic information with production records.
Over the past two years, we have both taught courses on Jay Z. At the University of Texas at Arlington, in #theJayZclass, we draw on the Jay Z dataset to compliment our close reads, distant reads, and general analysis of the rapper.
In our classes, our Jay Z dataset informs our discussions and enhances our close and distant reads of the rapper. It’s one thing to talk about Jay Z’s growth over the years, but actually pinpointing specific changes in his flow, his vocabulary, and song content clarifies and expands our considerations of his output.
Among other discoveries, our dataset reveals that
• Across his 12 solo albums, Jay and his producers use 276 samples.
• At 39%, Jay Z’s most frequent samples come from hip hop music.
• Soul music, at 30%, constitutes his next most frequent source of samples
One of the main implications of our Jay Z dataset concerns approaches to major authors in African American literary studies. Even though scholars in the field regularly concentrate on major authors, they tend to do so one major work at a time. Richard Wright’s Native Son or Toni Morrison’s Beloved. Or a few canonical poems by Langston Hughes and Gwendolyn Brooks. Our Jay Z dataset raises the possibility of looking at dozens and dozens of works by a single major author. Our dataset raises the possibility of moving beyond the so-called masterpieces of major authors and instead concentrating on their productivity and output over an extended time period.
How might African American literary scholars do more to engage DH? Or and at the same time, how might DH do more to adequately address Af-Am literature, in this case, verbal or language arts?
Examinations of the interconnectivity of collaborators and language patterns that comprise Jay Z’s music might serve as a model for tracking interrelated features of works by various black writers and verbal artists. Collecting metadata related to production aspects of more traditional texts might reveal broad trends about the collaborative efforts in African American literature over extended periods of time.
• The Jay Z Dataset--presentation at the University of Notre Dame