Recently, automated communication on social media has seen increased attention. Social bots, social media accounts controlled by algorithms that mimic human behaviour, have been found to attempt to influence users in several political contexts. However, their use in a commercial context, e.g. to boost sales of a product by aggressively promoting it with thousands of messages, has so far been neglected. To address this shortcoming, this paper examines the case of the social media music platform SoundCloud. We gathered a dataset of six months of activity, comprising 15,850,069 tracks and 12,125,095 comments. We then calculated a comment uniqueness score for highly active accounts to assess the variability of their comments. First analyses show that some accounts post suspiciously repetitive comments. These accounts also frequently repost existing content, but contribute little original content. An analysis of the commenting network further underlines that these accounts differ clearly from regular users. We conclude that the comment uniqueness metric can be used as an indicator to distinguish bots from humans, and that a considerable proportion of SoundCloud comments are likely to emanate from bots or semi-automated accounts. The implications of these findings and future plans are discussed.