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

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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

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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.

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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 or geospatial factors, or use popularity in terms of one user’s (or a small group of people’s) preferences. In this thesis, we define popularity as how popular a song is from a music chart perspective, including data from all Spotify listeners in a region (or on a global level).

As a summary of the discussed related work, the majority of visualization methods for musical information are founded upon differing types of content- or context-based attributes or quantifying the similarity between various pieces of music. Geographical data is rarely taken into consideration, and visualizing listening habits in conjunction with geospatial data in a scientific manner is not a common method (as noted by Hauger et al. [20]). The solution proposed in this thesis is based on the visualization of music popularity trends in time and space, which is a subarea of information visualization. A discussion on this background is presented in Chapter 3.

1.3 Problem formulation

The service SpotifyCharts.com offers a large amount of useful data to music enthusiasts, but it does not offer visualization or any other type of aggregation or summarization of their data. This means that record companies, artists, or anyone who has an interest in the area have to rely on browsing the charts individually, in their original textual form, to gain an understanding of how,

7 for example, a single by an artist performs on the music charts nationally, and around the world. Individual charts can show, at any single time, a small number of the most popular songs for a specific geographical region (or a global aggregation) in a short time span—either a day or a week. This interface does not facilitate other more complex exploratory tasks, such as: getting an overview of the data over larger time spans; comparing the performance of different songs and/or artists over time; or connecting the time and space characteristics of the popularity of songs and/or artists.

This identified gap can be decomposed into the following two research questions:

1. How can we develop a tool that provides visual representations of data from music charts to support exploratory analyses of trends over time and space?

2. Can the created visual representations provide valuable insights on the chart performances of songs and artists?

These research questions are henceforth referred to in this thesis as RQ1 and RQ2.

1.4 Motivation

Contemporary music charts on a national level have been around for many decades. In the U.S, Billboard published its first national music chart on July 27, 1940 [21], while in Sweden the first national music chart was in 1975 with Sverigetopplistan [7]. However, given the related work discussed in the previous section, it seems that compiling these temporal music charts into visualizations has not attracted much interest. This could be, in part, due to the

8 lack of access of the general public to the data which supports these music charts, something which Spotify has changed since the company provides music charts like these both in the Spotify client and through SpotifyCharts.com [8].

This thesis aims to propose one possible solution for representing and interacting with temporal music charts. Our main goal is to expand upon the tools supplied today for monitoring and gathering information of charting of popular music, for example, Spotify’s “Discover” section where users can find and browse songs and artists. We believe that visualization is an interesting and under-explored way to analyze this type of data and, through the development and evaluation of our prototype, we aim to provide an initial step of design ideas and a solution that could help raise interest in the area.

1.5 Results

We propose a new way of representing music charts over time, in the form of visualizations realized through a tool developed during the process of creating this thesis. We evaluate this developed tool via a method that involved demonstrating it for test subjects, asking them to perform certain tasks with the tool, and then asking the subjects to fill out a standard questionnaire (ICE-T) as defined by Wall et al.’s Value-Driven Visualization Evaluation [22]. This methodology is further discussed in Section 2.2.3.

1.6 Scope/Limitation

Scope. The music chart data collected is limited to the streaming service Spotify, and in temporal terms only as far back as 1 January 2017. Therefore, the examples showcased, and songs, albums, or artists selected are based on what artists and songs were in the top 200 music charts for the period 1 January

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2017 – 31 December 2020. In terms of national charts available, it is also limited to the countries where Spotify is available. Furthermore, due to the sheer amount (70 countries) of national music charts available, not all countries are included. The biggest music markets in terms of export and import are prioritized. This is further discussed in Section 4.1.

Limitations. The tool proposed in this thesis is an initial prototype (or a proof of concept) that uses simple and familiar visualizations. The user experience and the visualizations could be improved with more development iterations, including gathering requirements for the tool directly with the target users, which was not done. The user study used to measure the value of the visualization tool was a small-scale procedure, with only eight participants in total, and no information on their background or music interests was gathered. More participants would have allowed us to obtain more statistically significant results. Additionally, the prototype was not compared to any other existing similar tool, because we could not find a suitable candidate during our literature review and search for related works. Such a comparison might have brought to light more interesting results, weaknesses, and opportunities for improvement.

1.7 Target group

In this thesis, we consider music consumers as our target group, i.e. anyone who might be interested in using tools that provide insight into music charts and their trends. Other potential target groups for spatiotemporal visualizations of music charts could be music streaming services, such as Spotify1; music

1 https://www.spotify.com/

10 companies, such as Universal Music2; or music creators and/or artists (but they are not considered in this initial work).

1.8 Outline

This report is organized as follows. In Chapter 2 we discuss the methodological framework and research methods employed. Chapter 3 provides a theoretical background for the project and the knowledge gap which it intends to reduce. In Chapter 4 we discuss the implementation of the data-gathering script, the implementation of the visualization tool, and the demonstration and survey which results we present in Chapter 5. Chapters 6 and 7 analyze and discuss the results and knowledge gained from this thesis work, as well as discuss potential future work. In Chapter 8 we conclude the thesis work.

2 https://www.universalmusic.com/

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2 Method

2.1 Research Project

The catalyst of this thesis work was the knowledge gap identified between historic music charts on a global and national level, and the limited existing visual representations of these music charts over time. In the interest of reducing this knowledge gap, a design science methodology was employed. In design science (DS), there are six clearly defined steps [23]:

1. Problem identification and motivation In this thesis, the problem, as mentioned, is the identified gap between the availability of data from music charts for popular music and the lack of available tools to visualize spatiotemporal trends in these music charts in new ways. The motivation for this was to expand upon the subject and to provide the groundwork for future research in the area.

2. Definition of the objectives for a solution The objective of the solution to this problem was to provide a tool for creating visualizations from gathered data to supply the target users with new insights into the development of the gathered music charts over time and space. In a short summary, the requirements that guided the design of the visualization tool were to support users in: (1) Getting an overview of the chart data over large time spans; (2) Comparing the performance of different songs and/or artists over time; and (3) Connecting the time and space characteristics of the popularity of songs. These requirements were not gathered in any formal or structured way, and were derived

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mainly from the characteristics of the dataset itself and the experiences of the thesis author and supervisor.

3. Design and development The artifact is the visualization tool that supports creating visualizations of these music charts, in accordance with user input of what artist, song, and time limitations may be of interest to the target user. Gathering the data necessary for this became an objective in itself to fit into the greater objective of producing this prototype.

4. Demonstration After producing some versions of the prototype of the artifact to be created, the artifact was deployed to be evaluated by the target users specified in Section 1.7 (i.e. music consumers and the public in general). The artifact was hosted online, available via a web address, to act as a demonstration of the artifact.

5. Evaluation To evaluate the artifact, the Value-Driven Visualization Evaluation methodology (visvalue, in short) was used [22], which is centered around a standard questionnaire for user feedback on visualizations (the ICE-T). Details are provided in Section 2.2.3.

6. Communication The results of the aforementioned evaluation were communicated in Chapters 5 and 6 of this thesis.

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2.2 Research methods

The methods used in this thesis can be subdivided into three main parts:

2.2.1 Gathering data The website SpotifyCharts.com [8] provided a way to easily access temporal data for historic daily or weekly Spotify charts. The lists available are: Daily top 200, Weekly top 200, Daily viral 50, and Weekly viral 50. These charts are available and updated daily in the Spotify client; however, the client itself does not provide historical data, only the current, contemporary charts which are updated on a daily/weekly basis. The charts available in the ordinary Spotify client are also limited to the top 50 songs of the day.

The Daily top 200-chart lists the 200 most listened songs on Spotify of the past day. Furthermore, these charts are divided into each separate Spotify market. There is a global chart, and national charts such as the US, Great Britain, South Korea, Sweden, etc. (around 30-40 national charts in total).

In order to gather this data, a script written in the programming language Python3 is used to download chart data from a selection of the available markets on SpotifyCharts [8]. This data is then stored in a database.

Since the visualizations created by the tool depends entirely on this data, this part of the method is one important piece in the process of answering RQ1.

2.2.2 Analyzing and visualizing data When all the data is gathered, it must be analyzed and visualized in manners that aim to tell a story that is not apparent from just looking at the raw data. The large amount of data was pre-processed into a new representation of the

3 https://www.python.org/

14 data. This was achieved through the development of a web application written in JavaScript4. The implementation of this proposed visualization prototype is a central part of answering RQ1 and is discussed in more details in Chapter 4.

2.2.3 Validating To assess the value of the prototype, this thesis employed Value-Driven Visualization Evaluation (or visvalue, in short). This methodology was created with the intention to help researchers, designers, and practitioners to determine the value of visualizations. It is centered around the ICE-T questionnaire, which is composed of four different sections: Insight, Confidence, Essence, and Time, which in turn are composed of questions that are answered on a 7- point Likert scale, where 1 represents “Strongly Disagree” and 7 represents “Strongly Agree”. The template of the ICE-T questionnaire is supplied in Appendix B of this thesis [22] [24].

In more details, the ICE-T’s four sections are, according to Wall et al. [24]: • I – “A visualization’s ability to spur and discover insights and/or insightful questions about the data.” • C – “A visualization’s ability to generate confidence, knowledge, and trust about the data, its domain and context.” • E – “A visualization’s ability to convey an overall essence or take-away sense of the data.” • T – “A visualization’s ability to minimize the total time needed to answer a wide variety of questions about the data.”

To interpret the scores of the ICE-T questionnaire, Wall et al. [24] suggest that an average score of 5 or above in any of the four ICE-T categories speak for a strength in the visualization, while an average score of 4 or lower represents a

4 https://www.javascript.com/

15 weakness in the visualization. Based on their research, for visualizations to be deemed as valuable and/or good, they should result in an overall collective average score of 5 or higher, while visualizations that result in an overall collective average score of 4 or lower should be reconsidered and have their design revised. We revisit this score interpretation in Chapter 6 where the questionnaire results are analyzed.

The overall procedure consisted of (1) gathering individual participants to partake in the evaluation, (2) demonstrating the prototype, (3) suggesting some specific tasks for participants to perform, then (4) each of the participants filled the ICE-T questionnaire to evaluate their experience of using the demonstration.

Participants were gathered via Linnaeus University’s workspace in Slack, known as CoursePress. There, in the #general channel, which as of June 4, 2021, has 4,625 members, a message was posted inviting potential participants and explaining this thesis work, its intent, what was expected of each participant, and a screenshot of the application. Participants were then gathered either by reacting to the post with a “hand up” reaction, or by sending a direct message. The message posted is featured in Appendix C of this thesis.

The experiment was conducted asynchronously and remotely, due to the COVID-19 pandemic which was ongoing during the writing of this thesis. Each volunteer was sent a Google Forms link containing a link to the web- hosted prototype visualization tool, the instructions for the tasks to be performed (creating certain visualizations to motivate showcasing particular functionalities), and the ICE-T questionnaire formatted into Google Forms- questions. Each participant was also allowed to play with the tool in any way they wanted, in addition to these instructions. Eight participants in total

16 performed the instruction tasks, filled in all the ICE-T questions, and completed the user study. A link to this form, and a reproduction of its components and structure is featured in Appendix D.

The validation part of the methodology used in this thesis is intended to support in answering RQ2.

2.3 Reliability and Validity

Concerning the reliability of this thesis work, it is important to mention some points, mainly touching upon the gathering of data and the representation of data in the visualization tool.

When gathering the data, there were some issues of gaps in the timelines. For example, when downloading the daily music charts for the national chart of Brazil, there appeared to be some sort of temporary outage on the end of Spotify, where data for an extended period of time would be missing (a few months, in this case). To address this, the script had to be run again at a later date to fill the gaps in the incomplete temporal data. Furthermore, there is no guarantee that SpotifyCharts will continue its operations in the future, which would undermine the entire data-gathering part of this thesis work, since it completely relies on SpotifyCharts and its supply of temporal music charts. However, we possess a local backup of the data gathered, so this dilemma would only affect future data.

Moving on, the design process of the visualization tool was in its nature exploratory. This exploratory process is an inherent part of Design Science [23]. It is almost impossible for an exploratory process to yield the same results, even if all the variables that were surrounding it were the same. In our case, they include the author, the time during which the work is performed, and

17 the contemporary technology. This also presents us with some problems for reliability, which must be taken into consideration.

Regarding the survey which was used to evaluate the value of the thesis work, the reliability may also be negatively affected by the people who participated in this survey. The number of people who participated, the individual personality of each test subject, and something as simple as the mood the test subject was currently in when filling out the survey questionnaire are all factors that may have impacted the reliability of the results yielded.

When considering the validity of this thesis, the evaluation method of visvalue [22] is of great assistance. The score of each of the sub-questions of the survey questionnaire can be compiled and analyzed, and the value of the visualizations determined. This way of evaluating visualizations was developed with keeping the integrity of validity intact in mind. Using a standardized, well-supported instrument is superior to developing a custom, specifically made questionnaire for this thesis. Although the usage of a standardized questionnaire might not completely represent the tool in every way, we employed a methodology that has been validated and proposed by the scientific community as a qualitative way to evaluate visualizations, so we eliminate the risk of making a customized, but imprecise and invalid form.

2.4 Ethical Considerations

The ethical considerations of this thesis were related to the validating part of the methodology. The confidentiality and anonymity of each of the test subjects were secured through a Google forms-questionnaire, which offers functionality to make answers to the questionnaire completely anonymous. This utility was used to realize the form supplied through visvalue and distribute it to participants.

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3 Theoretical Background

The concept of using visualization and visual analytics to analyze and provide a way of understanding time-dependent data has been shown to be successful for a long time [25]. Visual analytics can be defined as a multifaceted research area where scientists that specialize in information visualization, scientific visualization, and geographic visualization intimately work together with researchers from analytical backgrounds, for instance, statistical analysis and modeling, geographical analysis and modeling, and machine learning and data mining, in finding new solutions to complicated problems on a societal scale. Geo-spatial visual analytics (or geovisual analytics, in short) aims to solve problems involving geographical space and events, objects, and processes populating this geographical space. Since the majority of the objects occupying space either arise or change in time, geovisual analytics must give apt attentiveness to time and the relationship between space and time [26].

Visual analytics intends to merge the strengths of electronic data processing, such as the modern computer is capable of, and human processing [1], which “can be characterized as an information-processing system, which encodes input, operates on that information, stores and retrieves it from memory, and produces output in terms of actions.” [2] Visualization, as a medium for humans and computers to converse and cooperate through graphic representations, is the manner through which this can be achieved. To analyze Spatio-temporal (which infers to time and space, where spatial refers to space and temporal refers to time) data and producing solutions to Spatio-temporal problems, seamless and sophisticated synergies are essential [1].

Today, analysis of temporal data which changes through space and time is not limited to professional analysts [1]. To give an example related to this thesis work, Spotify offers a yearly analysis of the users listening habits through their

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“YYYY Wrapped”-feature [27] (where YYYY is the current year, 2020 at the time of writing of this thesis). Spotify itself defines it as “a special hub in the app with some cool stats on the songs, artists, and podcasts you discovered throughout 2020.” [27]. This supports that numerous citizens would be interested in taking part in the Spatio-temporal analysis [1].

From the point of view of the researcher, the goal is to find techniques to mitigate the complexity of the topical data and discover ways to make analytical tools available and easily used for the wide community of prospective users, to encourage Spatio-temporal thinking and contribute to solving a wide array of problems [1].

To provide a way for users to interact and explore geographical spatial- temporal information, user interfaces (or UI, for short) can be used as tools to fulfill that goal. Suitable user interfaces for uncovering the potential of spatial- temporal geovisual analytics tools are integral if they are to be used efficiently and effectively [28].

In the visualization tool developed in conjunction with the writing of this thesis, which is outlined in Section 4.2, a map of the world is featured. In this map chart, each country where respective Daily Top 200 data was available, was colored in accordance with how high the actual data attribute for that respective country was. By data attribute, that entails a song by an artist, on a scale from 1-200, which represents the position of the particular song on the Daily Top 200 on that particular date which was currently selected in the map chart.

The colormap chosen to represent this 1-200 interval, was the Viridis colormap [29]. The reasoning behind the usage of this particular colormap was that it is

20 a perceptually uniform colormap, i.e. a colormap where equal steps in data are perceived by the human brain as equal steps in the color space, which has been found as the best choice regarding colormap for the majority of applications [30]. This can be motivated by research that has found that the human mind perceives changes in lightness as changes in data much better than, for example, changes in hue. Therefore, colormaps that have uniformly increases in lightness over the scale of the colormap are clearer for the viewer. The perceptually uniform colormap is an excellent example of such a colormap [29] [30].

This “world map”-visualization of geographical data was prompted by virtue of the usage of maps offering a recognizable and familiar way to present data separated by regions, such as continents or countries, which can be overlayed with coloring or heat maps in order to relay information to the viewer [15]. The usage of a world map, in particular, was motivated by the fact that the data gathered spanned countries on almost every continent, and thus could not be limited to only, for example, Europe.

Furthermore, since in our case the geo-spatial data also changes over time, cartographic animation has been employed. This has emerged as an effective visualization technique through its innate capability to show interrelations between geo-spatial data’s locations, attributes, and time. While these types of animations have been employed in communicating geo-spatial temporal information, they also have been impeded by the lack of interactivity for the user [31]. In light of this, we have provided the user with tools to interact with the geo-spatial temporal data after its initial manifestation.

The other part of the visualizations employed in the tool outlined in Section 4.2 features a line chart. This line chart was motivated because it a simple and

21 familiar visualization technique for time series data. These charts are in nature intuitive and make it easy to discern key events corresponding to lines on the x- and y-axis [32]. The simplicity and comprehensibility of a line chart are important to our application of such a chart, in which the x-axis represents time in the shape of isolated dates, and the y-axis the charting position of a song. However, because of this inherent simplicity, this is a further provocation of employing the aforementioned map chart as a way to complement this uncomplicated way of visualizing data.

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4 Research project – Implementation

4.1 Gathering Data

Firstly, to realize the implementation of the tool to be used to target the goal of this thesis, the raw data from Spotify had to be gathered and compiled into a manageable collection. To accomplish this, a script in the programming language Python [33] was developed. The reasoning for using this particular programming language is the experience of the author with the language, and because of the existence of a very useful (and related) python package by the name of fycharts [34]. A python package can be defined as pre-defined bits and pieces of code that can easily be integrated with newly developed code.

What fycharts provided was a way to extract chart data from the Daily Top 200 music chart lists from SpotifyCharts.com. This package was developed to fill the gap left by Spotify when the streaming service deprecated their official Spotify charts API [34] (short for Application Programming Interface). One could say that this, then, was the “unofficial Spotify Charts API”. The API provides ways to easily target Daily Top 200-lists, delimited by country, start of date range, and end of date range.

The application developed with the aid of fycharts downloaded national Daily Top 200 music chart lists which were provided by SpotifyCharts.com and compiled these lists into a database. The date range for the data stored in this database was 2017-01-01 to 2020-12-31. This interval was chosen due to SpotifyCharts.com’s temporal data only dating back to the beginning of 2017. The end of the date range was chosen regarding the time constraints imposed on this thesis work, which was February through May 2021. Instead of intermittently updating the chart data, for example, monthly, the decision was made to use the end of 2020 as an end of the date range. This is also in part

23 because 3 full years was sufficient, and a more well-defined interval to be used in the visualization tool, instead of having a few months of 2021-data featured as well.

We mentioned earlier the usage of a database. The decision of using a database was made due to the ease of accessing data through SQL [35], which is an industry-encompassing and tested tool to communicate with and fetch data from a database. SQL provides many ways of filtering data according to, in our example, artist, song, start date, end date, and national region, and in addition in a fast manner.

However, there were some complications in relation to the development and usage of this data-gathering application. Before all national Daily Top 200 lists could be gathered, SpotifyCharts.com implemented some changes to how data could be fetched from their website. A DDoS mitigation service (in SpotifyCharts.com’s case, Cloudflare) [36] was employed by the website, to prevent malicious attacks intended to overflow the website with excessive requests and as a result – create a denial-of-service in which the website becomes unavailable to its intended users.

There were some efforts made in this thesis work to find a way around this protection service to gather more national Daily Top 200 lists for usage in the upcoming visualization tool – however, due to time constraints of this thesis, a decision was made against it. Therefore, in the visualization tool, not all national markets where Spotify is available are fully featured. Additionally, in a few select cases, such as data from the Brazil market, there are some instances of data being incomplete.

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4.2 Visualizing data

The second part of the implementation of this thesis consisted of developing a tool for visualizing the data gathered in the previous sub-chapter in order to support the investigation of spatiotemporal trends.

This visualization tool was developed in the programming language JavaScript [37], divided into a client-side application as the presentation layer accessible through a web browser, and a server-side application as the data access layer. This software structure is often referred to as front-end and back-end, respectively in the software development industry. The user interface of the presentation layer is shown in Figure 1.

4.2.1 Presentation layer

Figure 1. An overview of the complete user interface.

The front-end part of the application handles interpreting data that is requested and delivered from the back-end. These requests and what exact data are to be requested are defined by the user by a small interface when accessing the application through a web browser. The user can input; Artist, Song, Start date,

25 and End date. The user can then press a button with the label “search”, the application forwards these filters and communicates with the server-side to fetch the requested data according to these parameters. This part of the user interface is shown in Figure 2.

Figure 2. The input part of the user interface.

The website then presents the data in two ways: one way is by showing a line chart with each national music chart where the requested song and/or artist has charted over time for that specific period, the other way is a geovisual representation where each national music chart is instead shown as each respective country on a world map, with a colormap scale representing the position for a specific song released by a specific artist, and a playable “time slider to visualize changes over time. These different avenues of visualization are shown in Figure 3 and Figure 5, respectively.

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Figure 3: A snapshot of the line chart representation of the web application for visualizing temporal music charts. The song “Without You (feat. Sandro Cavazza)” by the artist “Avicii” is showcased as an example, during its first month of charting between its release on 2017-08-10 and 2017-09-10.

The line chart depiction of the data features a y-axis defining the chart position on a scale of 1-200, representing the daily position of a song in the Spotify Daily Top 200-list. The x-axis of the line chart represents the date, an interval defined by the user as discussed previously. For each song, artist, and national music chart combination a line is generated. Most of the time, several lines are generated and are distinct by their varying coloring. At the bottom of the line chart, a legend is shown explaining to the user what line pertains to what song by an artist in a specific country. The line chart demonstration features interactive functionalities in the form of hovering over lines and specific data points to isolate them, and the ability to exclude lines entirely from the chart

27 to improve visibility in the user interface. This functionality is demonstrated in Figure 4.

Figure 4: The same showcased example as in Figure 2, however, most of the national markets have been excluded, and additionally, the mouse cursor hovering over the “U.S.A – Aug 23, 2017” data point is used to highlight the specific line, and to prominently show the chart position for that specific day.

The map chart representation of the search result data features a depiction of a map of the world, where each time series of an artist and song search over a specified period is instead represented by coloring each respective country in different intensities according to how high the song charted on a scale from 1- 200. The user, additionally, is presented with a slider at the bottom of the visualization with an associated “play/pause” button. The user can either press this button, to play an automatic animation where the map changes the coloring

28 of countries interactively according to each respective charting position, or use the mouse cursor to drag the slider left and right to provide a way to control more specifically what date to represent in the chart. This map chart is shown in Figure 5.

Figure 5. The map chart part of the visualization tool.

This answers how we visualize temporal and geospatial data over time for a specific song, which is part of RQ2. For specific artist searches, the visualization focuses on the line chart part of the tool, due its ability, for example, to display single releases before an upcoming album. This is highlighted in Figure 6.

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Figure 6. The Swedish artist “Veronica Maggio” chart performance in Sweden from 2019-03-22 to 2019-08-01. This showcases the performance of singles released before the upcoming album, which came out on 2019-06-14.

Moreover, the map chart features interactive functionalities beyond the time- slider feature. Each country can be hovered over by utilizing the mouse cursor to show the current chart position more specifically, and buttons labeled “+” and “-“ can be used to zoom in on specific geographical regions. These capabilities are shown in Figure 7.

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Figure 7. The map chart is zoomed in on Europe, and the mouse cursor is hovering over the Nordic country Sweden to show the specific chart position for that current day.

The scale of 1-200 on the map projection is visualized using the colormap Viridis. As previously discussed in Chapter 3, this particular colormap has been chosen on the grounds of it being a perceptually uniform colormap, which has been proven to be a clearer and superior choice to many other, available colormaps [29] [30].

The visualizations presented in the web application utilizes the framework HighCharts [38], which is a JavaScript library supporting a plethora of different charts and visualizations for JavaScript developers to feature in their

31 applications. Without using this library, the visualizations featured in this web application would most likely not have been realized, due to the sheer amount of time that would have been required to implement the types of visualizations utilized. Given that time was a finite resource in this thesis work, HighCharts was of great assistance.

There was a very large number of JavaScript visualization libraries available during the development of this software application. The reasoning behind the choice of using HighCharts in particular was because of the library’s excellent “Charts in Motion” functionality [39], which was a considerable catalyst for actualizing the “map chart over time”-part of the visualization tool developed.

4.2.2 Data access layer Regarding the server-side part of the application, this layer interprets the user- defined requests discussed in the previous sub-chapter and communicates with a database containing all gathered, locally stored data from SpotifyCharts.com to respond to the client with the requested data. To achieve this, SQL was employed to fetch data from a database, as previously discussed in this chapter.

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5 Results

The results presented in this section were gathered from eight participants, which evaluated the prototype according to the methodology described in Section 2.2.3. The ICE-T form used in the study is composed of 21 questions, all on a Likert scale from 1-7, where each number represents:

1 = Strongly Disagree 2 = Disagree 3 = Somewhat Disagree 4 = Neither Agree nor Disagree 5 = Somewhat Agree 6 = Agree 7 = Strongly Agree Unanswered question = Not Applicable

The questions are divided into four categories: Insight (8 questions), Confidence (5 questions), Essence (4 questions), and Time (4 questions).

5.2 Aggregated results

As suggested by the authors of the method [24], we begin by presenting the aggregated results for each level of the hierarchy, with both mean and median scores. These results are shown in Tables 1 to 4. The questions have been numbered according to the first letter of each question’s respective category (I, C, E, or T) and their order in the form. These abbreviations are also used in Chapter 6, in the discussion of the results.

The overall mean score, considering all questions/categories, was 5.35, and the overall median score was 5.0.

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Table 1: ICE-T scores for the Insight category. Question Mean Median score score I.1 The visualization exposes individual data cases and their 5.75 6.0 attributes I.2 The visualization facilitates perceiving relationships in the 5.87 6.0 data like patterns & distributions of the variables

I.3 The visualization promotes exploring relationships between 6.00 6.5 individual data cases as well as different groupings of data cases I.4 The visualization helps generate data-driven questions 4.75 5.0

I.5 The visualization helps identify unusual or unexpected, yet 5.75 5.5 valid, data characteristics or values I.6 The visualization provides useful interactive capabilities to 5.25 5.0 help investigate the data in multiple ways I.7 The visualization shows multiple perspectives about the data 4.62 4.5 I.8 The visualization uses an effective representation of the data 5.37 5.5 that shows related and partially related data cases

Aggregated scores for Insight category 5.42 5.0

Table 2: ICE-T scores for the Confidence category. Question Mean Median score score

C.1 The visualization uses meaningful and accurate visual 6.12 7.0 encodings to represent the data C.2 The visualization avoids using misleading representations 5.75 6.5

C.3 The visualization promotes understanding data domain 4.28 4.0 characteristics beyond the individual data cases and attributes C.4 If there were data issues like unexpected, duplicate, missing, 2.71 2.0 or invalid data, the visualization would highlight those issues Aggregated mean score for Confidence category 4.71 5.0

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Table 3: ICE-T scores for the Essence category.

Question Mean Median score score E.1 The visualization provides a comprehensive and accessible 5.25 5.0 overview of the data E.2 The visualization presents the data by providing a 6.12 6.0 meaningful visual schema E.3 The visualization facilitates generalizations and 5.00 5.0 extrapolations of patterns and conclusions E.4 The visualization helps understand how variables relate in 5.75 6.0 order to accomplish different analytic tasks Aggregated mean score for Essence category 5.53 6.0

Table 4: ICE-T scores for the Time category. Question Mean Median score score

T.1 The visualization provides a meaningful spatial organization 6.00 6.0 of the data T.2 The visualization shows key characteristics of the data at a 5.75 5.5 glance T.3 The interface supports using different attributes of the data 4.87 5.0 to reorganize the visualization's appearance T.4 The visualization supports smooth transitions between 4.87 4.5 different levels of detail in viewing the data T.5 The visualization avoids complex commands and textual 6.00 6.5 queries by providing direct interaction with the data representation Aggregated mean score for Time category 5.50 6.0

The detailed distributions of the participant-given ICE-T scores per question/category are presented in Figures 8 to 10. In more details, the scores for the Insight category are shown in Figure 8; the scores for the Confidence

35 and Essence categories are shown together in Figure 9; and the scores for the Time category are shown in Figure 10.

Figure 8: Distributions of ICE-T scores for the Insight category.

Figure 9: Distributions of ICE-T scores for the Confidence and Essence categories.

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Figure 10: Distributions of ICE-T scores for the Time category.

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6 Analysis

To analyze the data gathered in this thesis, the questions formulated in Section 1.3 are used as support for this section. Additionally, the results of ICE-T questionnaire answered by survey participants in Chapter 5 are analyzed.

RQ1: How can we develop a tool that provides visual representations of data from music charts to support exploratory analyses of trends over time and space?

Collecting the data needed for the visualization was mostly successful. Large amounts of spatiotemporal data were gathered and stored into a database, which utilized SQL to query the gathered data for analysis. This database was then successfully used as the basis for a web-based visualization prototype.

A prototype of a visualization tool featuring a time series (as a line chart) and a geographical chart as coordinated views, both displaying changes over time in the data, was the result of the effort of answering this question. The motivation behind the usage of the types of charts employed was discussed in Chapter 3. This, together with implementation descriptions presented in Chapter 4, are our answers to the first research question of this thesis, RQ1.

However, since this is quite a broad question, the discussion on how valuable these visualizations are (according to the value-driven methodology by Wall et al. [24]) is probably a more important aspect of the thesis.

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RQ2: Can the created visual representations provide valuable insights on the chart performances of songs and artists?

To provide evidence for this question, we discuss here the results for each of the questionnaire’s categories (as featured in Chapter 5), then conclude with a discussion on the general aggregated results at the end of this chapter.

Insight: In terms of insight into the data featured in the visualizations, the survey results in this category showed answers generally trending more towards Agree than Disagree. The median score of the answers was 5.0 (“Somewhat Agree”), while the average answer calculated from all questions in this category results in 5.42 (between “Somewhat Agree” and “Agree”). Following Wall et al.’s [24] interpretation that a score of 5 or more represents a strength of the visualization, the data suggests that the prototype provided overall good insight into visualizing the data.

In terms of strengths, questions I.1, I.2, I.3, I.5 all performed above the average of 5.42. This evidence seems to allude to that the visualization performed well in isolating data cases, the relationships between the data in these data cases, and identifying unexpected outcomes when generating visualizations. This, most likely, has to do with the functionality of isolating single song + artist combinations in the tool, as well as surprising the user with representing the raw data in the shape of the visualizations.

The weaknesses of providing insight into the data via visualization were related to promoting data-driven questions and showing multiple perspectives about the data. This could be because whilst a new way of viewing the data is provided, it does not promote further thinking or give rise to new questions in

39 relation to the visualization. Furthermore, the perspectives shown in this current iteration of the tool, are quite one-dimensional in what they portray. It should be mentioned, however, that these “weaknesses” still trend towards Agree, above the midpoint of “Neither Agree nor Disagree”.

Confidence: The confidence category had a median score of 5.0, but was the lowest scoring category out of the four regarding the average score, with 4.71 (between “Neither Agree nor Disagree” and “Somewhat Agree”). This suggests that confidence is the category with the most potential for improvement.

The strongest links in terms of confidence, as alluded to by questions C.1 and C.2, relate to meaningful and accurate representations of the data, and avoiding the use of misleading representations. This could be because of the linear scaling of the y-axis in the line chart, and the usage of the Viridis colormap in the geographical bisection of the visualization.

The one question which without a doubt resulted in the lowest score of the study was C.4, which relates to data issues such as duplicate, missing, or invalid data, and whether the visualization would reveal such issues or not. This could be related to issues with gaps in the timelines, especially in the case of Brazil, which was mentioned and discussed in Section 2.3. In addition, if a song which had a big gap in charting, such as songs that only chart during the Christmas weeks of the year, was displayed in the visualizations, a line would still be drawn over the entire calendar year, even if there were no chart entries of that particular song from, for example, January to November.

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Essence: The essence category also resulted in favorable results, with a median score of 6.0 and an average of 5.53 (between “Somewhat agree” and “Agree”).

The scores of the individual questions were quite close to each other in this category, which suggests that in terms of essence, the tool was quite consistent in a positive way. The strongest quality, supported by question E.2, seems to relate to the tool providing a meaningful visual schema, which could be by virtue of the type of charts chosen and especially how these charts complement each other to provide value.

The weakest link in the essence category, based on question E.3, concerns generalizations of patterns. Since similar song performance “curves” are not grouped, but separated by time, this could be a contributing factor to this weakness. If the line chart had a way of aggregating similar curves, this could most likely result in a higher average score for this particular question.

Time: Finally, when it comes to the time category of the ICE-T form, the survey results similarly showed answers generally trending more towards Agree than Disagree. The median score was 6.0 and the average answer calculated from all questions in this category resulted in 5.5 (between “Somewhat Agree” and “Agree”). This suggests that the tool does a good job of portraying time.

The strengths here are supported by the results of questions T.1, T.2, and T.5. The data points to spatial organization, showing key characteristics, and avoiding text-based queries and complex commands to interact with the tool are all strengths. This could be due to the map chart representation, a clear way

41 of showing the user the actual data attributes, and the “point-and-click” interaction functionalities available once a search has been performed.

In terms of weaknesses, supported by the results of questions T.3 and T.4, the visualizations scored lower when it came to supporting different attributes of the data to reorganize the appearance of the visualization, and supporting smooth transitions between viewing different levels of detail in the data. This could a result of the data attributes featured being quite limited, and to view a different level of detail, for example, a wider or narrower time span, the user has to do a new search instead of, for example, being able to zoom in on the line chart.

Overall: To conclude this chapter, we analyze the overall results of the evaluation in light of Wall et al.’s [24] recommendations on how to interpret ICE-T answers. As mentioned before, the authors of the methodology point out that good visualizations should strive for an overall average score of 5 or more. With a mean overall score of 5.35, and a median of 5.0, we believe that the data from the ICE-T suggests that the developed prototype fulfilled its goal of being an initial viable spatiotemporal visualization tool for music charts. As such, this concludes our answer for RQ2 with a positive outcome.

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7 Discussion & Future Work

The analysis of the results in the previous section shows that, despite its limitations, the tool prompted positive reactions to the proposed visualization of spatiotemporal trends of music charts and, according to the methodology, the prototype was considered a viable (or minimally valuable) initial solution.

Originally, more than the final 12 national or global Daily Top 200-lists gathered was planned. To find a suitable trade-off between the most relevant data to support interesting visualizations, and avoiding excessive amounts of data, the top 20 music markets rated by retail value in US$, as defined by the IFPI Global music report 2018 [40], were prioritized. However, due to complications discussed in Chapter 4, the United Kingdom, Australia, and the Netherlands, which were rated at positions #4, #8, and #11 by the IFPI rating, are missing from the gathered data.

The decision to use simple and familiar visualizations was taken to keep the initial prototype as accessible as possible, but it also incurs on disadvantages due to the techniques’ weaknesses when handling large amounts of data. The line charts, for example, may become cluttered and hard to read when performing searches that span a longer period. Another problem is the fact that, when an excessive number of lines are shown, there are not enough categorical colors to characterize each of them individually, so colors must be repeated.

Ways of mitigating these problems should be considered in future work. Aggregations, for example, are one possible solution. Longer periods could use weekly averages instead of daily positions, and/or displaying fewer or even single lines on the plot map. The map chart visualization might be used for artist-only searches as well (with input into the song-field omitted), where the

43 daily regional position would consist of the average daily position of all songs charting that day for that particular artist.

Another solution might be the use of more advanced visualizations, with different approaches on how to present spatiotemporal trends. For instance, HighCharts, the visualization framework utilized for creating this visualization tool, has at least a few hundred different types of visualizations, some of which could most likely be used to find other unexpected and unexplored insights into the spatiotemporal music chart data.

The use of machine learning [41] together with interactive visualization might also bring interesting possibilities in this context. A machine learning algorithm could be employed to find similarities between and categorize songs, albums, or artists in ways which from the perspective of a human person could not be found. These similarities can then be used together with advanced visualizations and unsupervised learning in order to bring forward complex and multidimensional patterns in the data.

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8 Conclusions

This thesis work has taken a visualization-based approach to analyzing music chart statistics, providing a deeper understanding and finding new, unexpected insights about the development of the popularity of popular music, using the streaming service Spotify as a way of gathering measurements of the popularity of contemporary songs. In this thesis, the results of gathering the data, developing the tool which features both a line chart representation and a geo-spatial world map representation of this data, and the conducted Value of Visualization-survey are presented. This adds to the area of research of visualization, with a focus on change over time of charting of popular, present- day music, and opens up avenues for future research in the area.

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References

[1] N. Andrienko, U. Demsar, D. Dransch, J. Dykes, S. I. Fabrikant, M. Jern, M.-J. Kraak, H. Schumann and C. Tominski, "Space, time and visual analytics", Geospatial Visual Analytics: Focus on Time Special Issue of the ICA Commission on GeoVisualization, vol. 24, no. 10, pp. 1577-1600, Oct. 2010.

[2] R. W. Proctor and K.-P. L. Vu, Human Information Processing, SpringerLink, 2012. Accessed on: May 12, 2021. [Online]. Available: https://link.springer.com/referenceworkentry/10.1007%2F978-1-4419-1428- 6_722

[3] A. Maasø and A. N. Hagen, "Metrics and decision-making in music streaming", Popular Communication, vol. 18, no. 1, pp. 18-31, Dec. 2019.

[4] The Nielsen company (US), LLC., U.S. Music mid-year report 2017, The Nielsen company (US), LLC., 2017. Accessed on June 4, 2021. [Online]. Available: https://www.nielsen.com/us/en/insights/report/2017/us-music- mid-year-report-2017/ [5] The Nielsen company (US), LLC., Time with tunes: How technology is driving music consumption, The Nielsen company (US), LLC., 2017. Accessed on June 4, 2021. [Online]. Available: https://www.nielsen.com/us/en/insights/article/2017/time-with-tunes-how- technology-is-driving-music-consumption/

46

[6] Billboard, Billboard Charts, Billboard Media, LLC, 2021. Accessed on May 2, 2021. [Online]. Available: https://www.billboard.com/charts

[7] IFPI Sverige, Sverigetopplistan, IFPI Sverige, 2021. Accessed May 2, 2021. [Online]. Available: https://www.sverigetopplistan.se/

[8] Spotify, SpotifyCharts.com, Spotify AB, 2021. Accessed May 2, 2021. [Online]. Available: https://spotifycharts.com/

[9] H. Hinterberger, Data Visualization, 2009. Accessed May 21, 2021. [Online]. Available: https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-39940- 9_1370

[10] R. Khulusi, J. Kusnick, C. Meinecke, C. Gillmann, J. Focht and S. Jänicke, ”A Survey on Visualizations for Musical Data”, Computer Graphics Forum, vol. 39, no. 6, pp. 82-110, Mar. 2020. [11] J. Li, Music Analysis through Visualization, 2016. Accessed on May 21, 2021. [Online]. Available: http://tenor2016.tenor- conference.org/papers/30_Li_tenor2016.pdf

[12] T. Bergstrom, K. Karahalios and J. C. Hart, "Isochords: visualizing structure in music", Proceedings of Graphics Interface 2007, vol. 33, no. 1, pp. 297-304, May 2007.

[13] C. Muelder, T. Provan and K.-L. Ma, "Content Based Graph Visualization of Audio Data for Music Library Navigation", 2010 IEEE International Symposium on Multimedia, vol. 1, no. 1, pp. 129-136, 13 Dec. 2010.

[14] M. Hamasaki, M. Goto and T. Nakano, "Songrium: a music browsing assistance service with interactive visualization and exploration of protect a

47 web of music", WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web, vol. 23, no. 1, pp. 523-528, Apr. 2014.

[15] D. Mashima, S. Kobourov and Y. Hu, "Visualizing Dynamic Data with Maps", IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 9, pp. 1424-1437, Sep. 2012. [16] D. Sprague, F. Wu and M. Tory, ”Music selection using the PartyVote democratic jukebox”, Proceedings of the working conference on Advanced Visual Interfaces, vol. 1, no. 1, pp. 433-436, Jan. 2008. [17] J. Zhang and D. Liu, Visual Analyses of Music History: A User-Centric Approach, 2017. Accessed on June 4, 2021. [Online]. Available: https://arxiv.org/abs/1703.07534 [18] D. Baur and F. Seiffert, ”The Streams of Our Lives: Visualizing Listening Histories in Context”, IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 1119-1128, Nov 2010. [19] D. Baur and F. Seiffert, ”Pulling strings from a tangle: visualizing a personal music listening history”, Proceedings of the 14th international conference on Intelligent user interfaces, vol. 1, no. 1, pp. 439-444, Feb 2009. [20] D. Hauger and M. Schedl, ”Exploring Geospatial Music Listening Patterns in Microblog Data”, International Workshop on Adaptive Multimedia Retrieval, vol. 1, no. 1, pp. 133-146, Oct. 2014. [21] Billboard, The First Billboard: All That Was 'New, Bright and Interesting On the Boards', Billboard Media, LLC, 2021. Accessed on: May 2, 2021. [Online]. Available:

48 https://www.billboard.com/articles/columns/chart-beat/6746273/first- billboard-issue-november-1-1894

[22] E. Wall, J. S. M. Agnihotri, A. Endert, L. Matzen, K. Divis and M. Haass, Value-Driven Visualization Evaluation, 2021. Accessed on: March 7, 2021. [Online]. Available: http://visvalue.org/

[23] K. Peffers, T. Tuunanen, M. A. Rothenberger and S. Chatterjee, "A Design Science Research Methodology for Information Systems Research", Journal of Management Information Systems, vol. 24, no. 3, pp. 45-78, 2007.

[24] E. Wall, M. Agnihotri, L. Matzen, K. Divis, M. Haass, A. Endert and J. Stasko, A Heuristic Approach to Value-Driven Evaluation of Visualizations, 2018. Accessed on: March 7, 2021. [Online]. Available: https://www.cc.gatech.edu/~john.stasko/papers/infovis18-icet.pdf

[25] W. Müller and H. Schumann, "Visualization for modeling and simulation: visualization methods for time-dependent data - an overview", WSC '03: Proceedings of the 35th conference on Winter simulation: driving innovation, vol. 35, no. 1, pp. 737-745, Dec. 2003.

[26] G. Andrienko, N. Andrienko, D. Keim, A. M. MacEachren and S. Wrobel, "Challenging problems of geospatial visual analytics", Journal of Visual Languages & Computing, vol. 22, no. 4, pp. 251-256, 2011.

[27] Spotify, Your 2020 Wrapped FAQ, Spotify AB, 2020. Accessed on: May 12, 2021. [Online]. Available: https://community.spotify.com/t5/FAQs/Your- 2020-Wrapped-FAQ/ta-p/5084997

[28] W. Cartwright, J. Crampton, G. Gartner, S. Miller, K. Mitchell, E. Siekierska and J. Wood, "Geospatial Information Visualization User Interface Issues", Cartography and Geographic Information Science, vol. 28, no. 1, pp. 45-60, Mar. 2013.

[29] Y. Liu and J. Heer, "Somewhere Over the Rainbow: An Empirical Assessment of Quantitative Colormaps", CHI '18: Proceedings of the 2018

49

CHI Conference on Human Factors in Computing Systems, vol. 1, no. 1, pp. 598-610, Apr. 2018.

[30] J. Hunter, D. Dale, E. Firing and M. Droettboom, Choosing Colormaps in Matplotlib, Matplotlib, 2021. Accessed on: May 15, 2021 [Online]. Available: https://matplotlib.org/stable/tutorials/colors/colormaps.html

[31] P. Ogao and M.-J. Kraak, "Defining visualization operations for temporal cartographic animation design", International Journal of Applied Earth Observation and Geoinformation, vol. 4, no. 1, pp. 23-31, Aug. 2002.

[32] D. A. Keim, T. Nietzschmann, N. Schelwies, J. Schneidewind, T. Schreck and H. Ziegler, "A Spectral Visualization System for Analyzing

Financial Time Series Data", Eurographics/IEEE TCVG Symposium on Visualization, vol. 1, no. 1, pp. 195-202, May 2006.

[33] Python Software Foundation, Python, Python Software Foundation, 2021. Accessed on: May 15, 2021. [Online]. Available: https://www.python.org/

[34] K. Gakuo, fycharts, Python Package Index, January 1, 2021. Accessed on: May 15, 2021. [Online]. Available: https://pypi.org/project/fycharts/

[35] TechnologyAdvice, SQLCourse, TechnologyAdvice, 2021. Accessed on: May 15, 2021. [Online]. Available: http://www.sqlcourse.com/intro.html

[36] Cloudflare, Inc., Cloudflare - The Web Performance & Security Company, Cloudflare, Inc., 2021. Accessed on: May 15, 2021. [Online]. Available: https://www.cloudflare.com/

[37] JavaScript.com, JavaScript.com, JavaScript.com, 2021. Accessed on: May 16, 2021. [Online]. Available: https://www.javascript.com/

[38] Highcharts, Interactive JavaScript charts for your webpage, Highsoft, 2021. Accessed on: May 16, 2021. [Online]. Available: https://www.highcharts.com/

50

[39] L. Cabrera, Charts in Motion, HighCharts, August 20, 2015. Accessed on: May 16, 2021. [Online]. Available: https://www.highcharts.com/blog/tutorials/176-charts-in-motion/

[40] SAS Institute Inc., Machine Learning: What it is and why it matters, SAS Institute Inc., 2021. Accessed on: May 3, 2021. [Online]. Available: https://www.sas.com/en_us/insights/analytics/machine-learning.html

[41] IFPI, IFPI Global music report 2018, IFPI, 2018. Accessed on: May 18, 2021. [Online]. Available: https://www.riaj.or.jp/riaj/open/open- record!file?fid=1638

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Appendix A

Link to the visualization tool

The developed visualization tool can be found at: https://spotify-charts-visualize.herokuapp.com/

A few screenshots of the application are shown below.

Screenshots of the visualization tool.

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Appendix B

Value of Visualization form

The Value of Visualization survey form used to create the Google Forms used to conduct the user survey in this thesis.

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Appendix C

Gathering participants message (note: translated from Swedish)

”Hello everyone! I am currently developing a tool that creates visualizations of how songs / artists' list placement on daily music charts moves over time on the music streaming service Spotify, in connection with my bachelor thesis. As part of the work, I perform a survey to evaluate the value and usefulness of the visualizations that the tool generates. In connection with this, I am looking for people to try out the tool, and then fill in a form with a number of questions. No previous knowledge is required at all, quite the opposite! All levels are welcome, tell your classmates, your partner, your friends and your family if you want! The more the merrier! Does that sound interesting? React to on this post with a “hand”-emoji, throw me a dm here on Slack, or comment on this post, and I will contact you with more details! I am also offering a small sneak peek right now:“

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Appendix D

Google forms ICE-T questionnaire https://docs.google.com/forms/d/1hHGNJLjZo3pcJ7ffnWP9FVt-Vp_xaGWiaZEwypVZSrs/

SpotiVis - Evaluation form This visualization tool aims to provide new insights into the charting positions of popular music on the streaming service Spotify, using data gathered from Spotify's "Daily Top 200"- lists. The tool features a line chart showing chart position over time on a global as well as national level, and a Map chart that visualizes national chart position development over time for a specific song.

This form is completely anonymous. Feel free to share the link to this form with anyone you know, the more participants, the better!

Instructions To get started, navigate to the URL, fill in an artist or song, select the dates you’re interested in, hit search. A chart should appear in a few moments. The "chart position" on the y-axis of the line chart represents the position that particular song had in the respective "Daily Top 200"-list for that specific country.

You can isolate a single song on a national market by hovering over it in the line chart. You can omit a country from the line chart by pressing its name in the bottom of the line chart visualization. To navigate in time on the map chart, either drag the slider at the bottom or press the button on the left of the slider to play an animation. You can hover over each colored country its respective daily chart position. You may zoom in on the map by using the "+/-"-keys on the left. NOTE: not all countries where Spotify is available are included.

We provide some recommended searches to provide some initial, meaningful visualizations. Kindly enter these recommendations in order to showcase the full functionality of the visualization tool.

Worldwide "big" single release: Artist: "avicii"

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Song: "without you" Date start: 2017-08-10 Date end: 2017-09-30

National "big" single release: Artist: (leave empty) Song: "tillfälligheter" Date start: 2019-05-01 Date end: 2019-12-23

Singles leading up to an album release (only line chart): Artist: "veronica maggio" Song: (leave empty) Date start: 2019-03-22 Date end: 2019-06-30

Seasonal (christmas) song: Artist: "john lennon" Song: "xmas" Date start: 2017-01-01 Date end: 2019-12-31

You are of course free to play around and use the visualization tool in any way you'd like in addition to these recommendations. For example, try searching for only artists, only songs, and different date intervals (note: shorter date intervals usually provide more meaningful visualizations)... The sky is the limit!

Questions Each question is to be answered on a scale from 1-7 where each number represents: 1 = Strongly Disagree 2 = Disagree 3 = Somewhat Disagree 4 = Neither Agree nor Disagree 5 = Somewhat Agree

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6 = Agree 7 = Strongly Agree Unanswered question = Not Applicable

If you do not feel confident about answering a certain question, feel free to leave it unanswered to represent "Not applicable".

Terminology: Individual data cases: A search of an artist/song, or both Attributes: Position in a respective country

The rest of the form consists of the questions in the ICE-T questionnaire featured in Appendix B.

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