Abusive language detection has become an important tool for the cultivation of safe online platforms. We investigate the interaction of annotation quality and classifier performance. We use a new, fine-grained annotation scheme that allows us to distinguish between abusive language and colloquial uses of profanity that are not meant to harm. Our results show a tendency of crowd workers to overuse the abusive class, which creates an unrealistic class balance and affects classification accuracy. We also investigate different methods of distinguishing between explicit and implicit abuse and show lexicon-based approaches either over- or under-estimate the proportion of explicit abuse in data sets.