Meinecke, ChristoferSchebera, JeremiasEschrich, JakobWiegreffe, DanielKrone, MichaelLenti, SimoneSchmidt, Johanna2022-06-022022-06-022022978-3-03868-185-4https://doi.org/10.2312/evp.20221129https://diglib.eg.org:443/handle/10.2312/evp20221129Rap music is one of the biggest music genres in the world today. Since the early days of rap music, references not only to pop culture but also to other rap artists have been an integral part of the lyrics' artistry. In addition, rap musicians reference each other by adopting fragments of lyrics, for example, to give credit. This kind of text reuse can be used to create connections between individual artists. Due to the large amount of lyrics, only automated detection methods can efficiently detect similarities between the songs and the artists. Here, we present a visualization system for analyzing rap music lyrics. We also trained a network tailored specifically for rap lyrics to compute similarities in lyrics. Here a video of the prototype can be seen.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing --> Visualization application domains; Visualization systems and tools; Applied computing --> Sound and music computing; Document management and text processingHuman centered computingVisualization application domainsVisualization systems and toolsApplied computingSound and music computingDocument management and text processing10.2312/evp.2022112995-973 pages