Challenges in Discriminating Profanity from Hate Speech
This addresses a nuanced challenge in content moderation for social media platforms, though it is incremental as it builds on existing classification techniques with a new dataset.
The study tackled the problem of distinguishing general profanity from hate speech in social media, achieving 80% accuracy in a 3-class classification task using supervised methods with features like n-grams and ensemble classifiers.
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.