Machine Learning Suites for Online Toxicity Detection
This work provides a comparative analysis for online toxicity detection, but it is incremental as it focuses on evaluating existing methods rather than introducing new ones.
The study systematically evaluated 62 classifiers from 19 algorithmic families on the Jigsaw dataset to identify toxic online comments, finding that tree-based algorithms offer the best explainability and a simple bad word list is the most predictive feature among 28 tested.
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.