scholarly journals More of the Same – On Spotify Radio

2017 ◽  
Vol 9 (2) ◽  
pp. 184-211 ◽  
Author(s):  
Pelle Snickars

Spotify Radio allows users to find new music within Spotify’s vast back-catalogue, offering a potential infinite avenue of discovery. Nevertheless, the radio service has also been disliked and accused of playing the same artists over and over. We decided to set up an experiment with the purpose to explore the possible limitations found within “infinite archives” of music streaming services. Our hypothesis was that Spotify Radio appears to consist of an infinite series of songs. It claims to be personalised and never-ending, yet music seems to be delivered in limited loop patterns. What would such loop patterns look like? Are Spotify Radio’s music loops finite or infinite? How many tracks (or steps) does a normal loop consist of? To answer these research questions, at Umeå University’s digital humanities hub, Humlab, we set up an intervention using 160 bot listeners. Our bots were all Spotify Free users. They literally had no track record and were programmed to listen to different Swedish music from the 1970s. All bots were to document all subsequent tracks played in the radio loop and (inter)act within the Spotify Web client as an obedient bot listener, a liker, a disliker, and a skipper. The article describes different research strategies when dealing with proprietary data. Foremost, however, it empirically recounts the radio looping interventions set up at Humlab. Essentially, the article suggests a set of methodologies for performing humanist inquiry on big data and black-boxed media services that increasingly provide key delivery mechanisms for cultural materials. Spotify serves as a case in point, yet principally any other platform or service could be studied in similar ways. Using bots as research informants can be deployed within a range of different digital scholarship, so this article appeals not only to media or software studies scholars, but also to digitally inclined cultural studies such as the digital humanities.

Author(s):  
Pelle Snickars

Spotify Radio allows users to find new music within Spotify’s vast back-catalogue, offering a potential infinite avenue of discovery. Nevertheless, the radio service has also been disliked and accused of playing the same artists over and over. We decided to set up an experiment with the purpose to explore the possible limitations found within “infinite archives” of music streaming services. Our hypothesis was that Spotify Radio appears to consist of an infinite series of songs. It claims to be personalised and never-ending, yet music seems to be delivered in limited loop patterns. What would such loop patterns look like? Are Spotify Radio’s music loops finite or infinite? How many tracks (or steps) does a normal loop consist of? To answer these research questions, at Umeå University’s digital humanities hub, Humlab, we set up an intervention using 160 bot listeners. Our bots were all Spotify Free users. They literally had no track record and were programmed to listen to different Swedish music from the 1970s. All bots were to document all subsequent tracks played in the radio loop and (inter)act within the Spotify Web client as an obedient bot listener, a liker, a disliker, and a skipper. The article describes different research strategies when dealing with proprietary data. Foremost, however, it empirically recounts the radio looping interventions set up at Humlab. Essentially, the article suggests a set of methodologies for performing humanist inquiry on big data and black-boxed media services that increasingly provide key delivery mechanisms for cultural materials. Spotify serves as a case in point, yet principally any other platform or service could be studied in similar ways. Using bots as research informants can be deployed within a range of different digital scholarship, so this article appeals not only to media or software studies scholars, but also to digitally inclined cultural studies such as the digital humanities.


Author(s):  
Minoru Yoshida ◽  
Shogo Kohno ◽  
Kazuyuki Matsumoto ◽  
Kenji Kita

We propose a new music artist recommendation algorithm using Twitter profile texts. Today, music recommendation is provided in many music streaming services. In this paper, we propose a new recommendation algorithm for this music recommendation task. Our idea is to use Twitter profile texts to find appropriate artist names to recommend. We obtained word embedding vectors for each artist name by applying word2vec algorithm to the corpus obtained by collecting such user profile texts, resulting in vectors that reflect artist co-occurrence in the profile texts.


India is a very vast market for internet services as it has over 480 million active internet users in the country. Music streaming services in India is emerging day by day. The competition in the market is so high that even two giants Jio Music and Saavn join their hand in 2018 to provide a combine service all across the globe. In, 2019 a global giant Spotify entered into music streaming market in India and affected the each music service in India. Gaana owned by Times Internet have over 150 million active monthly users in the country while JioSaavn reported 100 million active monthly users as per a website. This research is going to study the market capture of various music streaming services in India. Currently, as per the research, Spotify is the most popular streaming service. As per the literature available on various platforms other streaming services were holding the major proportion of the Indian market but after the launch of Spotify, it became most loved streaming service. The research is being done to find out the existing music streaming services are affected by the entrance of Spotify or not


2021 ◽  
Vol 46 (2) ◽  
pp. 57-63
Author(s):  
Lotte Wilms ◽  
Caleb Derven ◽  
Merisa Martinez

How can European library staff working in digital humanities connect with peers in the library sector, determine where to find relevant information about digital scholarship, provide their collections as data and to be an equal partner in digital humanities research? The LIBER Digital Humanities Working Group was created as a participatory knowledge network in 2017 to address these questions. Through a series of workshops, knowledge sharing activities, and a Europe-wide survey and resulting report, the Working Group engaged with the international LIBER DH community. Useful reflections are provided on organising an open, voluntary DH community and planning for inclusive activities that benefit digital scholarship in European research libraries.


2018 ◽  
Vol 14 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Charlie C. Chen ◽  
Steven Leon ◽  
Makoto Nakayama

The proliferation of free on-demand music streaming services (e.g., Spotify) is offsetting the traditional revenue sources (e.g., purchases of downloads or CDs) of the music industry. In order to increase revenue and sustain business, the music industry is directing its efforts toward increasing paid subscriptions by converting free listeners into paying subscribers. However, most companies are struggling with these attempts because they lack a clear understanding of the psychological and social purchase motivations of consumers. This study compares and contrasts the two different phases of Millennial generation consumer behaviors: the alluring phase and the hooking phase. A survey was conducted with 73 paying users and 163 non-paying users of on-demand music streaming services. The authors' data analysis shows two separate behavioral dynamics seen between these groups of users. While social influence and attitude are primary drivers for the non-paying users in the alluring phase, facilitating conditions and communication control capacity play critical roles for the paying users in the hooking phase. These results imply that the music industry should apply different approaches to prospective and current customers of music streaming services.


2021 ◽  
Vol 29 (6) ◽  
pp. 0-0

This study investigates the causes impacting the consumers' intention of the premium music streaming services' subscription in China. An integrated model called the Theory of Streaming Service Acceptance (TSSA) is proposed to explain and predict premium music streaming service subscription behaviors. The TSSA consists of four constructs: attitude, descriptive norm, injunctive norm and perceived behavioral control. The research data was collected in the form of an online survey in China with 120 respondents. Then, interviews were conducted to collect qualitative data from 20 participants. An explanatory sequential mixed method was implemented and the PLS-SEM technique was used to analyze the survey data. The results showed that all constructs in modified research mode, including attitude, injunctive norm and perceived behavioral control except descriptive norm, are indicative predictors for a person’s intention toward premium music streaming services’ subscription. Significant practical inspirations from the perspective of music streaming services providers are also summarized.


2020 ◽  
Vol 18 (1) ◽  
pp. 29-42
Author(s):  
Elena Razlogova

Focusing on early experiments with algorithms and music streaming at WFMU, the longest-running US freeform radio station, and the Free Music Archive (FMA), a curated open music website, this article shows how commercial streaming services have been indebted to independent, open music infrastructures but have then erased and denied that history. The article ‘provincializes’ music streaming platforms such as Spotify by focusing not on their commercial aims but instead on the ‘convivial’, collaborative practices and spaces that their software engineers and users inhabited. I analyse an experimental national telephone broadcasting service at WFMU in 1989, an algorithmic WFMU radio stream ‘The Flaming Robot of Love’ during the Republican National Convention in 2004 and the ‘Free Music Archive Radio App’ that recommended tracks on the FMA website from 2011 to 2016. The app worked with an application programming interface (API) from Echo Nest. Echo Nests’ algorithmic recommendation engine also powers most commercial streaming services today. When Spotify purchased Echo Nest in 2014 and took the start-up’s open API offline in 2016, it engaged in ‘primitive accumulation’ of open-access knowledge and resources for commercial purposes. The FMA closed in 2019 and now only exists as a static site. As social institutions, however, WFMU and FMA ‘recomposed’ ‐ adapted to a new medium and a new political context ‐ collaborative engineering practices of the early broadcasting era. The article argues that moments of oppositional ‘conviviality’ in media culture such as the FMA should be analysed as elements of a continuous struggle.


2016 ◽  
Vol 30 (3) ◽  
pp. 282-297 ◽  
Author(s):  
Jon Welty Peachey ◽  
Adam Cohen

Research partnerships between scholars and sport for development and peace (SDP) organizations are common, but firsthand accounts of the challenges and barriers faced by scholars when forming and sustaining partnerships are rare. Therefore, the purpose of this study was to examine them, and to uncover strategies to overcome these challenges across different partnership contexts. Eight prominent SDP scholars were interviewed. Guided by collaboration theory and the partnership literature, findings revealed challenges included navigating the political and organizational landscape; securing commitments from organizations with limited resources; negotiating divergent goals, objectives, and understandings; and conducting long-term evaluations and research. Strategies to address these issues involved developing strategic partnerships, cultivating mutual understanding, building trust, starting small, finding the cause champion, and developing a track record of success. Key theoretical and practical implications are drawn forth, as well as intriguing future research directions.


Sign in / Sign up

Export Citation Format

Share Document