master thesis spotify

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Stack Music: Spotify and the Platformization of the Digital Music Commodity

Journal Title

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Digital platforms play an increasingly prevalent role in 21st century capitalism. They shape our search results, facilitate our communication habits, structure our workdays, reinforce our communities, and increasingly impact cultural life in ways that extend beyond mere communications. Their interfaces influence decisions about what films to watch, their algorithms recommend what songs and podcasts we enjoy, and their agreements with distributors frequently determine even the most basic access to digital media today. The current largest music streaming service in the world, Spotify plays an important role in cultural life today, asserting itself as a networked intermediary between users, advertisers, and the music industry in an effort to capitalize on the infrastructural aspects of cultural access through ad-supported and subscription- based music streaming options.

This thesis explores what Anne Helmond has called the “platformization” of digital media, with a specific focus on the Swedish music platform Spotify. Building on Jeremy Wade Morris’ notion of the “digital music commodity,” I argue that Spotify’s efforts to situate digital music within their own software system align with recent trends in the technical and intermediary structures known as platforms. The stack, the software scaffolding present beneath nearly all of today’s major platforms, offers a useful lens into discussions of such software intermediaries at scale, providing insight. into the material basis that platforms rely on in daily operation. Each chapter focuses on a single aspect of the organizing logic of Spotify, tracing the broad superstructure of the platform to its source in software tools. Across chapters on machine listening, recommendation algorithms, and digital platforms in totality, the work stresses the ways platforms have become “a new business model capable of extracting and controlling immense amounts of data” with the rise of these large, economy-shaping intermediaries.

Description

Arcand, Kyle Robert (2019). Stack Music: Spotify and the Platformization of the Digital Music Commodity . Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/18911 .

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Dukes student scholarship is made available to the public using a Creative Commons Attribution / Non-commercial / No derivative (CC-BY-NC-ND) license .

Master's thesis – Spotify

Spotify forever changed the way we think of music when releasing their music streaming platform. Their huge success has inevitably lead to big competitors like Apple, Amazon and Google to step up their game and enter the fight for listeners. Being a fast-growing product company like Spotify, with a fleet of 250 (and counting) autonomous teams in an agile environment, means great potential in terms of what can be done. However, steering the vessel becomes trickier for each additional team and demands more from the organizational structure, as well as from the suite of technical solutions. The Platform Developer Experience tribe at the company, with an overall goal of providing the best possible conditions for these teams to perform well, suspects that this autonomous set-up creates bottlenecks, ultimately hindering the end-to-end productivity. This tribe wanted us to find and better understand these bottlenecks, and furthermore to design and present features of an enterprise software aiding in their elimination. What: User research / Product design     When: January 2020 - June 2020

Project preparation

The project started by talking to several stakeholders to understand their expectations and view on the project to be carried out. Given the limited time frame of the project and the expected deliverables from Spotify’s side, as well as from Chalmers, a time plan had to be mapped out, including internal deadlines making sure we progressed as planned. The idea was to go through a full design thinking cycle, with an etnographic lense guiding the chosen methods for each phase.

User research

In order to fully understand and to be able to map out the problem space of productivity blockers, a thorough data collection had to be done. Since the users and target group of our study was developers of Spotify, it meant we had to talk to them. A lot of them. This was done primarily through semi-structured interviews, contextual inquiries and focus groups. The latter was considered more fruitful since it allowed several users to elaborate on each others ideas and it opened up for additional perspectives of a given topic. An important aspect, initially expressed by Spotify, was to talk to a wide spectrum of developers; ranging in tenure, coming from different discplines and office sites.

Trip to Manhattan

To not risk capturing a new ”Stockholm syndrome”, we traveled across the Atlantic to Spotify's office site in New York. During our visit, we ran additional interviews, focus groups and guerrilla research sessions across the different floors in 4 World Trade Center. We also got the chance to present our initial findings to the Platform Developer Expeirence teams based in New York. This opened up the chance for them to give feedback and further feed our knowledge of the problem space.

Data collected from 49 different developers was transcribed and analysed using affinity diagramming and thematic analysis. Quotes addressing similar issues were clustered and themes started to emerge. To risk mitigating bringing biases to the table, this was done seperately between the two of us for the first few transcriptions. The results were then compared and a shared code book was created to use on the rest of the data. The larger themes were evaluated based on their predicted return of investment (ROI) and impact. After close discussions with the main stakeholders, it was decided to move forward with the problem of code exploration & evaluation – one of the sub-problems of the umbrella theme Discoverability .

Problem statement

Developers have different ways of approaching exploration of code and reusable components, arguably because they are coming from different verticals and are writing different kinds of business code. But there seemed to be some shared issues that made the situation a lot trickier. In short, the problem of code exploration & evaluation could be divided to four fundamental issues, namely:

• It is difficult determining best practice exemplar.
• It is problematic to find example code.

• The current Spotify search tools are inadequate.
• Developers are missing a natural marketplace for reusables.

The lay as much of a good foundation as possible for the ideation phase, it was necessary to convert the problem of code exploration & evaluation to an actionable problem statement using Point of Views (POVs) and How might we questions. These were the two POVs brought forward:

• Developers at Spotify need to be able to more easily discover reusable code components because it would prevet them from having to reinvent the wheel.
• Developers at Spotify need to be able to evaluate code because it would help them in their choice of which code to utilize.

Using Crazy 8's and the POVs, we created a myriad of different ideas to explore the solution space. Despite having come up with a few promising ideas, we decided to invite a few developers and stakeholders to the ideation in a remote design workshop. What followed was the work of merging, abandoning and refining ideas. Once three concrete ideas had made it through the screening, six design critique sessions were held with developers. During theses sessions, the ideas were presented briefly and some low fidelity paper prototypes were shown. A discussion regarding their general thoughts and concerns followed, and these opinions were used as support when deciding the idea to proceed with.

The Use Case Marketplace

The idea we decided to move forward with was a new concept called The Use Case Marketplace . The idea is quite simple, instead of trying to find reusable code components through their names, which many times are arbitrary and far from descriptive, the developers can now discover reusables via a new storefront; namely their use case and intended functionality. This way, we utilize what the developers already know when starting their search journey, that is, what they are looking to do and what functionality they need, and remove the current necessity of knowing the name of the reusable. The concept was prototyped and taken from a low fidelity to a medium fidelity state. The medium fidility prototype was then user tested and evaluted with six Spotify developers. The developers were taken through a discovery journey of trying to find a use case, see picture below. The focus of the tests were to examine the usability and the test participants’ overall perception of the concept as a whole; was this something the developers could see themselves use? One of the major learnings was the need to rethink how a developer narrows down the number of results. Being able to add addtional filter chips both through the search bar and via the dropdown menus was percieved as confusing and needed to be addressed for the final prototype. Overall, the learnings from the tests were used to iterate and tweak the concept before taking it to a high fidelity state.

Final prototype

The Use Case Marketplace concept consists of two parts: (1) the marketplace where the developers search for and find potentially useful use cases, and (2) the use case page itself where the developer would read up on the use case.

Searching for use cases

A developer narrows down the number of use cases by adding keyword-based chips in the search bar, describing what functionality they are looking to achieve and the current constraints they have. The system then presents the developer with several cards, each representing a use case matching the specified chips.

The final concept was presented for Spotify stakeholders in the begin of June and was very well perceived. The future of the concept remains unclear, but the work to put more focus on discoverability across the Spotify ecosystem has already started. Alongside the final concept, 15 user experience factors to consider when designing for discoverability in enterprise software was extracted and served as the main result for the academia and the final thesis report. If you are interestest in the report and the 15 factors, feel free to let me know and I will share the report with you.

Keeping the Beat on: A Case Study of Spotify

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• Inês Gomes 19 ,
• Inês Pereira 19 ,
• Inês Soares 19 ,
• Mariana Antunes 19 &
• Manuel Au-Yong-Oliveira 20  

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1366))

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• World Conference on Information Systems and Technologies

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The music industry has faced a tremendous change ever since the emergence of streaming. People now had access to unlimited music to listen to and share with others, which is a great concept. As companies started to invest in the future of streaming, many platforms were created, one of them being Spotify. Standing amidst a crowd of competitors, Spotify managed to climb to the top and comfortably remain there over the years (listed on the NYSE and having 120 employees; IPO share price (3 rd April 2018) = 169.9 US Dollars and share price of 319.77 US dollars on 4 th December 2020). However, it was not easy. Being one of the top streaming platforms in the industry requires effort, investment, adaptation and innovation, and exceptional management. This study analyses in-depth the aforementioned aspects, as well as the company’s business models and its revenue, which combined, led Spotify to where it is today. A survey with 498 answers was performed regarding Spotify, and the results were analyzed by using descriptive and inferential (Chi-Square test) statistics. We thus conclude very confidently that there does seem to be an association (statistically significant at the 0.1% level) between age and the use of Spotify. Younger respondents (18–35 years) are more likely to use Spotify than older respondents (> 35 years). Overall, Spotify’s business model is one of the best in its field and is constantly evolving, which is what is to be expected of a company that seeks the top spot on the podium.

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Inês Gomes, Inês Pereira, Inês Soares & Mariana Antunes

INESC TEC, GOVCOPP, Department of Economics, Management, Industrial Engineering and Tourism, University of Aveiro, 3810-193, Aveiro, Portugal

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Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

Gintautas Dzemyda

DCT, Universidade Portucalense, Porto, Portugal

Fernando Moreira

Department of Information Sciences, University of Sheffield, Lisbon, Portugal

Ana Maria Ramalho Correia

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Gomes, I., Pereira, I., Soares, I., Antunes, M., Au-Yong-Oliveira, M. (2021). Keeping the Beat on: A Case Study of Spotify. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies . WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1366. Springer, Cham. https://doi.org/10.1007/978-3-030-72651-5_33

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Stanisljevic , D.

The impact of Spotify features on music discovery in the streaming platform age

Publication.

Music discovery and music consumption, in general, have changed significantly in the age of digitization. Physical record stores were replaced by online music streaming platforms. This evolution changed the music industry for both consumers and artists. The music industry shifted to a subscription-based business model, offering access to immense music libraries for a monthly fee. Spotify, as the biggest music streaming service, has had a large role in this shift. The music streaming giant incorporated techniques to guide users through their enormous music catalog by offering music recommendations. Research suggests that Spotify has caused an increase in quantity and diversity of music consumption. This thesis studies the role of the different Spotify music discovery features on the perceived impact of Spotify on music discovery among students. This research applies a cross-sectional research design with an online questionnaire that was filled out by 152 respondents. Students that actively use Spotify to consume music were sampled from the population. The data was analyzed by applying the statistical analyses; Pearson’s correlations, One sample T-test, and multivariate OLS-regression analyses. Two separate models were tested in the regression analyses measuring the relationship between the different Spotify music discovery features and the dependent variables measuring the perceived impact of Spotify on music discovery, and Spotify perceived as the most useful music discovery tool. The results of model 1 indicate that there is a significant positive relationship between the Spotify music discovery features discover weekly, and recommendations based on previous listening behavior and the dependent variable PSEMD. While the results of model 2 indicate that there is a significant positive relationship between the music discovery features discover weekly, recommendations based on previous listening behavior, popular playlists and the search bar, and the dependent variable PMUMDT. These results suggest that these Spotify music discovery features effect the perceived impact of Spotify on music discovery among students. With the music discovery feature, recommendations based on previous listening behavior having the strongest effect on the perceived impact of Spotify on music discovery.

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Master's thesis on Big Data

AdrianRiesco/Masters-Thesis-on-Big-Data

Folders and files, repository files navigation, extraction, transformation, loading and visualization of combined twitter and spotify data in a scalable architecture, master's degree in business intelligence and big data in secure environments.

This project corresponds to the Master's Thesis in Big Data and uses the social network Twitter to obtain information about the latest music listened to by users (by searching the #NowPlaying hashtag) and then query the track and artist data involved that Spotify , a music-as-a-service based platform, has. The entire process is managed using widely recognized tools within the field of Big Data.

The project can be downloaded and executed locally or accessed via Data Engineer Project . [Update August 08, 2022] The live example deployed in Google Cloud Platform has been paused to save costs.

⚠️ If you face any kind of problem or the web is running slowly, I encourage you to run the project in your local environment.

ℹ️ For more detail, please refer to the project report and the slides used for project defense on July 20, 2022.

Description

This project implements a ETL process to collect data from Twitter. The steps of the process are:

• The Twitter API is consulted to gather the tweets with the hashtag #NowPlaying. The name of the endpoint queried is recent search .
• The tweet is cleaned up, stopwords and other hashtags are removed, and the remaining text (which usually corresponds to the track name and artist) is isolated.
• The Spotify API is queried with the previously cleaned up text to collect the identified track information. The names of the endpoints queried are search for item and get tracks' audio features .
• The data is formatted and stored in a .csv file.
• The data is uploaded to Cassandra and the .csv is stored as a history file.
• The data served from Cassandra is requested by the back-end and served on the front-end .
• The data is displayed to the user on the front-end.

The project has been built using Docker and Docker Compose to run the following containers:

• Apache Airflow containers configured for flow orchestration: webserver, scheduler, worker, init, triggerer, redis, postgres, client, and flower. Airflow setup required a custom image with the following packages installed via PyPI as additional requirements: "apache-airflow-providers-apache-spark", "requests", "pandas", "cqlsh". In addition, in the Airflow Dockerfile, the Java SDK 11 was installed and the JAVA_HOME variable set. The image used as base image is the official Airflow image (version 2.3.0) found on DockerHub ( https://hub.docker.com/r/apache/airflow ), as well as the Docker Compose base file ( https://airflow.apache.org/docs/apache-airflow/stable/start/docker.html\#docker-compose-yaml ).
• Apache Spark containers were configured for data processing: master and three workers. Spark setup required a custom image with the following packages installed via PyPI as additional requirements: "requests", "pandas", "cqlsh". The image used as base image was the Bitnami Spark image (version 3.1.2) obtained from DocherHub ( https://hub.docker.com/r/bitnami/spark ).
• An Apache Cassandra container was configured for data storage, using an additional container to set up the database configuration. The image used was the official Cassandra (version 4.0) image found on DockerHub ( https://hub.docker.com/_/cassandra ) and no additional requirements were needed.
• A Linux container was configured for the web application. The container required a custom image with the following packages installed via PyPI as additional requirements: flask (version 2.1.2), cassandra-driver (version 3.25.0), flask-cqlalchemy (version 2.0.0), redis, Cmake, cryptography. The image used as base image was the official Python image (version "3.8-slim-buster") found on DockerHub ( https://hub.docker.com/_/python ).

Prerequisites

• Docker v20.10
• Docker-compose v1.29
• Python v3.8
• Twitter and Spotify APIs developer keys added in a file .env following the format of the .env.example file and located in the same folder.

The correct operation of the project with different versions is not guaranteed.

Clone the project, make sure that the "airflow/logs", "airflow/plugins" and "spark/resources/history" folders are set with the correct permissions so that Airflow can edit them, and run docker-compose up in the command console:

ℹ️ The commands described for the installation and usage of the project are oriented to Linux environments.

Once the project is deployed, three visual interfaces can be accessed that can help the user to better understand the process:

After the services are running, access to the Airflow UI and activate the DAG " spark_main ". After all the tasks are executed correctly, the data will be displayed in the web application views. If not paused, the DAG will be executed every 30 minutes.

To stop the project, run the following command within the docker folder:

To completely reset the project, run the following command within the docker folder:

Adrian Riesco Valbuena.

• Python 6.9%

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