SpotiBot—Turing testing Spotify (Snickars)

Producing and coding bot ‘listeners’ has today become almost as easy as automated music production has been for years. Machines can thus both ‘create’—and ‘listen’ to ‘music’ (whatever we mean by these categories). In fact, such notions are capricious within the contemporary streaming music landscape. Snickars present interest (spring 2016) within the project Streaming Heritage evolves around various forms of (semi-)automated music, sounds and (audio)bots – and essentially about Turing testing Spotify. Under the computational hood of streaming services all streams are equal, and every stream thus means (potentially) increased revenue from advertisers. One might, for example, ask what happens when — not if — streaming bots approximate human listener behavior in such a way that it becomes impossible to distinguish between a human and a machine? Streaming fraud, as it has been labeled, then runs the risk of undermining the economic revenue models of streaming services as Spotify.

During the last months Snickars has hence set up an experiment at HUMlab – SpotiBot — with the purpose to determine if it is possible to provoke, or even to some extent undermine, the Spotify business model (based on the 30 second royalty rule). Royalties from Spotify are only disbursed once a song is registered as a play, which happens after 30 seconds. The SpotiBot engine was be used to play a single track repeatedly (both self-produced music and Abba’s ”Dancing Queen”), during less and more than 30 seconds, and with a fixed repetition scheme running from 10 to n times, simultaneously with different Spotify account. Results from these experiments will be presented at a number of forthcoming conferences and in a article that is currently being written.