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Bachelor’s degree Project SpotiVis - Finding new ways of visualizing the spread of popular music Author: Dennis Fredsson Supervisor: Rafael Messias Martins Semester: Spring 2021 Subject: Computer Science Abstract Simply by reading data and statistics of the charting positions of popular songs on global and national music charts, it is hard to understand how the popularity of songs, albums, or artists within pop music truly behave over time. However, analyzing the data using visualizations as means of communication might provide us with new points of view and new insights into how the popularity of contemporary popular music behaves over a longer period. This is the hypothesis that we intend to investigate in this thesis. An interactive visualization application (presented as a website) has been developed based on data from “Daily Top 200” lists provided by Spotify. A survey was then used to evaluate the application, with the results suggesting that new and interesting insights into the trends in the popularity of music can be gained from the proposed prototype. Keywords: Visualization, Music, Spotify, Pop, Popular music, Chart, Streaming Service 2 Contents 1 Introduction ________________________________________________ 4 1.1 Background ___________________________________________ 4 1.2 Related work __________________________________________ 6 1.3 Problem formulation ____________________________________ 7 1.4 Motivation ____________________________________________ 8 1.5 Results _______________________________________________ 9 1.6 Scope/Limitation _______________________________________ 9 1.7 Target group _________________________________________ 10 1.8 Outline ______________________________________________ 11 2 Method __________________________________________________ 12 2.1 Research Project ______________________________________ 12 2.2 Research methods _____________________________________ 14 2.2.1 Gathering data _______________________________________ 14 2.2.2 Analyzing and visualizing data __________________________ 14 2.2.3 Validating ___________________________________________ 15 2.3 Reliability and Validity _________________________________ 17 2.4 Ethical Considerations __________________________________ 18 3 Theoretical Background _____________________________________ 19 4 Research project – Implementation_____________________________ 23 4.1 Gathering Data __________________________________________ 23 4.2 Visualizing data _________________________________________ 25 4.2.1 Presentation layer _____________________________________ 25 4.2.2 Data access layer _____________________________________ 32 5 Results ___________________________________________________ 33 5.2 Aggregated results _____________________________________ 33 6 Analysis __________________________________________________ 38 7 Discussion & Future Work ___________________________________ 43 8 Conclusions _______________________________________________ 45 References ___________________________________________________ 46 Appendix A __________________________________________________ 52 Link to the visualization tool ________________________________ 52 Appendix B __________________________________________________ 53 Value of Visualization form _________________________________ 53 Appendix C __________________________________________________ 54 Gathering participants message (note: translated from Swedish) _____ 54 Appendix D __________________________________________________ 55 Google forms ICE-T questionnaire ____________________________ 55 3 1 Introduction This is a Bachelor’s thesis in Computer Science, with a focus on visualization. The thesis aims to develop a visualization system to provide new insights into the "pulse of popular music" on a national as well as global level, using temporal data from the music listening service Spotify. Merely looking at the data from national music charts, or “top lists”, around the world, day by day, might not be an effective way of obtaining the big picture of how a song or an artist performs over time on national charts around the world. This is partly due to the large amount of data available. SpotifyCharts.com contains more than 30 million data points across its timeline starting on 1 January 2017, considering the different Top 200/Viral 50 lists, updated daily/weekly, in the 70 countries which has this data available. Exploring and interpreting such a large data set, given its geospatial characteristics, can be hard without appropriate support [1] [2]. Today, music labels use quite simple metrics to improve and analyze their data [3]. This thesis aims to investigate how visualization can be used to interpret and analyze data gathered from Spotify, in an attempt to gain a new perspective on how the spread of popular music ensues. Although we only take a small initial step into this area, we believe that the use of interactive visualization can, in the long term, potentially help music professionals use this type of data to their advantage to optimize or maximize the spread and popularity of new releases. 1.1 Background Spotify is one of the world’s leading audio and music streaming services, originating from Sweden. As of today, over 30 million songs are available for 4 listening, supported on all the most popular platforms (desktop and mobile). Since their advent, streaming services such as Spotify have provided easy access to music for the listeners. A computer or a phone with an internet connection is all that is necessary to access the massive music library which Spotify brings to its users. In the U.S, the introduction of technology offering the ability to stream music has increased the number of people listening to music from 2016 to 2017 and, in each year spanning from 2015 to 2017, the amount of music consumed by each individual has also dramatically increased [4] [5]. In order to provide a way of measuring the current most popular songs of today, music charts provided by websites/magazines such as Billboard [6] in the U.S.A, or Sverigetopplistan [7] in Sweden gather metrics and compiles them into a list of the current most popular songs. These metrics historically included sales of singles or albums, but with the advent of the music streaming industry, they now include digital metrics as well, such as digital sales, downloads, and streams. In this thesis, the charts used as brickwork for the data gathered are curated by Spotify itself and thus employ only digital streams as a metric. Spotify provides these charts through their website SpotifyCharts.com [8]. Visualization is any technique for creating images, diagrams, or animations to communicate a message [9]. This visual imagery provides an effective way of communicating both abstract and concrete ideas. Transforming temporal daily chart data into imagery through visualization to find new pathways of conveying chart performance of a piece of popular music is the challenge we are dealing with in this thesis. 5 1.2 Related work Music visualization is a large area of research, with a diverse range of subareas touching on different aspects of the challenge [10]. Some works focus, for example, on the structure of music itself, such as highlighting notable features in modern musical compositions [11] or conveying information about interval quality, chord quality, or chord progression in digital music [12]. These are not directly related to this thesis, since we focus on the visualization of a large- scale music collection instead of individual songs. Regarding the visualization of music collections, the existing works mainly focus on various attributes of musical information aiming to provide new perspectives on personal musical archives that go beyond simple plain file lists [10]. They assist in tasks such as editing, exploring, and navigating these collections. Muelder et al. [13] use visualization to provide a graph-based visual interface of a music listener’s digital music collection, based on the content of the music itself instead of pre-defined tag information (since they can often be incorrect or misleading). Songrium, a music browsing assistance service, uses visualization to explore what is referred to as a “Web of Music”. This Web of Music showcases the relationship between original pieces of music and derivative songs, offering a way to discover new music for the listener [14]. This thesis differs from these examples in that the features that we use for the visualizations do not come from the music attributes themselves, but from the geospatial trends in popularity of the songs and artists (i.e. their ranks, over time, in the top lists around the world). There has also been research done in visualizing music collections regarding popularity. Mashima et al. [15] gathered data from last.fm (a music recommendation service) from one point in time (July 2009) and visualized the popularity of the top 250 artists by mapping their similarity to 2D coordinates 6 and using font size to represent popularity. Sprague et al. [16] designed a “democratic music jukebox” with the purpose of giving all individuals present at social gatherings an equal influence over the music played. The collected votes were then visualized to group participants with similar music taste together, spreading social awareness. Zhang et al. [17] and Baur et al. [18] [19] performed work involving individual listeners’ listening history to develop visualizations of each user’s music listening history. These papers are related to this thesis, but they either do not include large-scale temporal