Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words

Common approaches to text categorization essentially rely either on n-gram counts or on word embeddings. This presents important difficulties in highly dynamic or quickly-interacting environments, where the appearance of new words and/or varied misspellings is the norm. A paradigmatic example of this situation is abusive online behavior, with social networks and media platforms struggling to effectively combat uncommon or nonblacklisted hate words. To better deal with these issues in those fast-paced environments, we propose using the error signal
of class-based language models as input to text classification algorithms. In particular, we train a next-character prediction model for any given class, and then exploit the error of such class-based models.
to inform a neural network classifier. This
way, we shift from the ability to describe
seen documents to the ability to predict
unseen content. Preliminary studies using out-of-vocabulary splits from abusive
tweet data show promising results, outperforming competitive text categorization
strategies by 4–11%.