Stance on topics such as the death penalty is often expressed by discussing subordinated or related targets (e.g. costs of execution). This implicit way of communication is typically modelled by defining a set of explicit targets, which are related to the topic (e.g. death is irreversible). As these sets can be created in different ways, it remains an open question whether all methods result in equally good target sets. Thus, we collect a new dataset of YouTube comments on the death penalty and annotate it with stance based on two target sets: (i) one expert set extracted from idebate.com and (ii) one representing the wisdom of the crowd from reddit.com. We systematically compare these two sets for reliability, coverage and topicality. We find that both sets have have strengths and weaknesses, but that they complement each other well in how they describe stance in our dataset. Our analysis shows that the composition of target sets is an important and non-trivial subtask of stance and sentiment analysis, which is worth investing efforts in.