  To identify and classify toxic online commentary, the modern tools of data
science transform raw text into key features from which either thresholding or
learning algorithms can make predictions for monitoring offensive
conversations. We systematically evaluate 62 classifiers representing 19 major
algorithmic families against features extracted from the Jigsaw dataset of
Wikipedia comments. We compare the classifiers based on statistically
significant differences in accuracy and relative execution time. Among these
classifiers for identifying toxic comments, tree-based algorithms provide the
most transparently explainable rules and rank-order the predictive contribution
of each feature. Among 28 features of syntax, sentiment, emotion and outlier
word dictionaries, a simple bad word list proves most predictive of offensive
commentary.
