Collecting and analysing data from SoundCloud

As >our qualitative research starts, so does our quantitative research. We have been collecting data from SoundCloud’s API (Application Programming Interface) which is the gateway to access SoundCloud’s data. We have also been updating the IF analysis code written by Daniel Allington for analysing network actions in Interactive Fiction communities, to make it useful for analysing what happens between users on SoundCloud. SoundCloud has made available an SDK (Software Development Kit) for Python and other programming languages, which is a set of functions and programs that we can use in our code to do things with SoundCloud data.

We can collect various data from SoundCloud including public data on their users, users’ tracks, groups they have joined, comments they have made on tracks and tracks they have liked, as well as who users follow and who follows them. With this last information – how users follow each other – we can use our updated version of the IF analysis code straightaway and analyse SoundCloud data.

We have collected a data sample of 500 users and their related data (tracks, groups, follows, etc), starting from a randomly chosen user and using snowball sampling to explore each user’s connections to other users.

The graphs below – (1), (2), (3), from left to right – are equivalent to the ones in Daniel’s forthcoming article on evaluative relationships between interactive fiction creators, and show:

  1. A graph of all users (the dots) and who they follow (the lines between the dots)
  2. The same graph as (1), but without the users who are not followed by any other users. This leaves a graph of users recognised by other recognised users.
  3. The same graph as (2), but with more users removed – those who do not follow any users. This leaves only those users that are really participating in the network.

The next steps are to collect considerably more data, use more of the information within the SoundCloud data such as favourited tracks and comments, and to scale our analysis and visualisations up to be able to deal with much larger collections of data. Our work-in-progress code will be available at and we will post regular updates on our progress on this website.


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