Visualising how users comment on tracks in SoundCloud

We have been exploring how visualisations can illustrate over time how users comment on tracks in SoundCloud. Commenting has been highlighted in our qualitative research as a way of building relationships and showing appreciation of other musicians’ work. In fact, initial inspection of a sample set of comments is showing that most comments in our samples tend to be positive or constructive, rather than overly critical or negative.

We have created two visualisations:

1. A 30 second snapshot of commenting activity in early years of SoundCloud

2. A longer video showing interactions over a number of years

These visualisations are based on data taken from a snowball sample of 1000 SoundCloud users, selected because they are interacting with each other in some way (commenting on each other’s tracks, following other users in the sample, liking other’s tracks, etc).

The circles are music tracks that users have uploaded to the SoundCloud music social networking site.

When a user adds a track, this is shown by a green flash between the user avatar and the circle representing the track.
When a user comments on a track, this is shown by a yellow/orange flash between the avatar and the track circle.
Groups of track circles clustered together represent where one user has uploaded multiple tracks to SoundCloud.

To see more, come to our free public event on 6 June!

The visualisations are generated using Gource.
The sample data are collected from the SoundCloud API using code on our github repository (our github repository is also listed on our Links page).
The videos have been created using Open Broadcast Software and Fraps.


2 thoughts on “Visualising how users comment on tracks in SoundCloud

  1. Interesting how users seem to comment so much on their own tracks – I guess they’re having conversations with people who are not in this particular sample. Which in turn shows just how enormous a sample has to be to encompass the breadth of value-producing interactions that go on, even with regard to a relatively constrained initial sample of users!


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