Exploring Music Rankings with Interactive Visualization
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Exploring Music Rankings with Interactive Visualization Leandro Guedes Carla M.D.S. Freitas Instituto de Informatica Instituto de Informatica Universidade Federal do Rio Grande do Sul Universidade Federal do Rio Grande do Sul (UFRGS) (UFRGS) Porto Alegre, Brazil Porto Alegre, Brazil leandro@guedes.com.br carla@inf.ufrgs.br ABSTRACT music services, Spotify [15] and YouTube [18], are the most Music rankings are mainly aimed at marketing purposes but popular ones. also help users in discovering new music as well as comparing In general, rankings of many different things were always songs, artists, albums, etc. This work presents an interac- available and have been used to influence users' choices; mu- tive way to visualize, find and compare music rankings using sic rankings (also called charts) would not be different. TV different techniques, as well as displaying music attributes. music channels, such as MTV and VH1, have most of their The technique was conceived after a remote survey that col- schedule based on music rankings: they show what most lected data about how people choose music. Our visualiza- people want to see. tion makes easier to obtain information about artists and tracks, and also to compare the data gathered from the two The Billboard [3] magazine produces the most famous mu- major music rankings, namely Billboard and Spotify. We sic ranking, the "Hot 100" list, which shows the most played also report the results of experiments with potential users. tracks (usually called singles, music that is being released on the media) based actually by streaming activity, radio air- play and sales data (respectively audience impressions mea- CCS Concepts sured and sales data compiled by Nielsen Music [6]). Spotify •Human-centered computing ! User interface de- also produces rankings, which are based on users' streams, sign; Visual analytics; Information visualization; and can be filtered by location, daily or weekly. The data is available at Spotify Charts [16]. Keywords These popular rankings reflect the marketing strategy of record labels. When data are easier to observe and com- Music visualization; Music rankings; Music charts; Interac- pare, new strategies can be planned and put into practice, tive visualization. contributing to improve marketing and music quality. Music rankings visualizations can help the analysis of data about 1. INTRODUCTION artists and record labels, and also work as recommendation systems: users can use visualizations to compare and clas- People listen to music everyday, some of them even all day sify artists' information. For example, if the user prefers long. Music became a huge industry, with several artists pop music, it is likely to be easier finding another pop music and groups competing for popularity and recognition, which only looking at an interactive visualization. Regarding rec- is likely to result in earnings. The more fans they conquer, ommendation, a tool can analyze what the user is listening the more influence they have. to and recommend other similar artists. Due to the worldwide internet access, the way people listen There are several works dealing with music visualization and to music is changing. Some years ago, the success of a certain analysis, but only a few are about music rankings. In our artist was mainly calculated by how many LPs or CDs were work we provide an interactive way to explore music rank- sold, which we call physical music sales. Nowadays, the ings, through different visualization techniques integrated main way of listening to music is using online streaming, within a web application, aiming at supporting artists data like websites/players such as Youtube and Spotify. In fact, and music exploration in general. Liikkanen and Aman˚ [12] found out that among on-demand The rest of the paper is organized as follows. Section 2 briefly reviews related works. In Section 3, we firstly intro- Permission to make digital or hard copies of all or part of this work for duce the results of a remote user survey that we developed personal or classroom use is granted without fee provided that copies for requirement analysis; then we explain how we have cho- are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy sen the current design, which data sets we decided to use, otherwise, or republish, to post on servers or to redistribute to lists, the implemented visualization techniques and the user in- requires prior specific permission and/or a fee. terface provided by our web application. In Section 4, we SAC 2017,April 03-07, 2017, Marrakech, Morocco describe and discuss the experiment with potential users, Copyright 2017 ACM 978-1-4503-4486-9/17/04. $15.00 and finally, in Section 5, we discuss our findings and draw http://dx.doi.org/10.1145/3019612.3019690 final comments including possibilities of future research. 2. RELATED WORK aim at supporting the comparison of rankings and, most This section presents a brief review of works that range from important, showing attributes of the music tracks in the exploration of music datasets to visualizing user's personal rankings, which is likely to make easier for a user to decide music listening history and automatic genre classification. between listening a different music, exploring new alterna- tives based on genre, artist and position in the ranking, for An interesting solution for the analysis of different rankings example, or following the known path of listening the same in general is LineUp [10]. The authors presented a ranking music tracks. scalable multi-attribute visualization technique based on bar charts. The technique allows tabular data sets to be sorted for creating rankings, where the attributes values are rep- 3. MUSIC RANKINGS VISUALIZATION resented by bars. Attributes can be grouped for sorting purposes, and different rankings for the same data set can People use rankings in general to compare data and get rec- be lined-up and compared. ommendations. Thus, the idea of using music datasets for building rankings is natural in the current streaming era. In One of the simplest tasks when dealing with music collec- 2015, at least two new big streaming services appeared, Ap- tions is navigation and/or exploration. Ono et al. use a ple Music and TIDAL, involving famous artists and record similarity graph [13] to enable the exploration of data sets labels. This has impacted the traditional rankings. Playlists in terms of hierarchical similarities. They built a methodol- from these streaming services work as imperceptible mer- ogy for users to visually explore music collections consider- chandising. The users are attracted by titles such as "Top ing that the similarity can take place only in small parts of 100", "Hottest tracks", "The most played tracks", and start the song. It uses Music Information Retrieval (MIR) to find listening to brand new tracks, resulting in a recommendation similar segments between pairs of audio files and a graph cycle. metaphor to display the detected similarities. In this work, we decided to use two datasets acquired from Often, when dealing with rankings, time is an important at- Billboard and Spotify. Billboard data were acquired with tribute because rankings can vary in time. This is especially a web crawler and stored in a MySQL database containing true for music rankings. Thus, it is rather common finding the track position, track name, artist, URL to listen on Spo- works that are based on time-line visualizations. For exam- tify, last week position, weeks on chart and peak position. ple, Dias et al. [7] combine a timeline-based visualization Spotify data were acquired from Spotify Charts as CSV files with a set of synchronized-views and an interactive filtering and also stored in the database, containing the track po- mechanism. Also, it is also interesting to observe how music sition, track name, artist, streams and URL. Music genre taste evolved along time, and this has also drawn attention was an extra information mapped with iTunes [1] to handle [14] and the visualization shown Billboard data. The data in data redundancy, and added to each artist data. We only that work is a time series starting in 1958: the top 5 artists considered the major music styles, so we would have a small for each week are shown in an interactive timeline and the amount of data to represent, making easier to identify genres tool plays automatically the number-one track of each week. by color. It is also possible to search a precise week, artist or track. As for user's personal music listening history, there are some 3.1 Requirement Analysis interesting works. LastHistory [2] is an interactive visual- A remote user survey was set up for 13 days to obtain data ization application for displaying Last.fm [11] data, the mu- about users' preferences and habits in listening music or se- sic listening stories, along with contextual information from lecting new music tracks to listen. A total of 377 people from personal photos and calendar entries. Last Chart! [8] also 11 countries answered our questionnaire. They were 23 years uses personal data from Last.fm, and displays Bubble, Cloud old in average. Similarly to Liikkanen and Aman˚ [12], we and other visualization charts on the web. Another example found out that Youtube (85%), download (67%) and Spotify is Peter Gilks' site [9] that shows data from the tracking of (45%) are the most used services to listen to music. They his own music consumption on Spotify using Last.fm. He are followed by Radio 35%, CD 21% and iTunes 18%. The uses a handy script to download last.fm data into a CSV for preference for Youtube might be explained because it is easy building the visualization.