  In this study we approach the problem of distinguishing general profanity
from hate speech in social media, something which has not been widely
considered. Using a new dataset annotated specifically for this task, we employ
supervised classification along with a set of features that includes n-grams,
skip-grams and clustering-based word representations. We apply approaches based
on single classifiers as well as more advanced ensemble classifiers and stacked
generalization, achieving the best result of 80% accuracy for this 3-class
classification task. Analysis of the results reveals that discriminating hate
speech and profanity is not a simple task, which may require features that
capture a deeper understanding of the text not always possible with surface
n-grams. The variability of gold labels in the annotated data, due to
differences in the subjective adjudications of the annotators, is also an
issue. Other directions for future work are discussed.
