| dc.description.abstract | This paper is a result of an investigation into a very popular Big Data service: The music streaming service Spotify. In music streaming services there are frequent and automatic moderations of content in the form of for example personalisation based on user interactions with the service. Personalisation is in this case recommendation systems that suggest new music based on what the user has already listened to. This thesis is a qualitative study of how music streaming Big Data can be seen as facts only after interacting with society in different ways. Public debate, advertising and marketing, consumer demands, science, history and culture are all societal forces with which Spotify is inevitably entangled and the aim of the analysis is to show how these forces are balanced to produce inscriptions society recognises as facts.
The empirical material and analytic framework in this thesis have been chosen to look at implications of the phenomenon Big Data for the internet. The study has mainly text as empirical material but different sources are used. To understand the interactions between society and digital technology I am using theory written by scholars affiliated with Science and Technology Studies (STS) such as Bruno Latour, Steve Woolgar, Evelyn Ruppert, John Law, Mike Savage, Susanne Bauer and Sissel Myklebust. The thesis is also a closer look at marketing and branding practices and ethical considerations related to the everyday internet.
The analysis shows that Spotify is affected by the phenomenon Big Data on different levels and that the service is optimised for collection and analysis of user logs. Also, in an effort to make personalisation and surveillance more accurate and reliable the materiality of the internet and what it means in society changes. With more focus on data analysis comes a more scientific – and therefore more real everyday internet, contrasting the idea of virtuality. | en_US |