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Annotating abusive language is expensive, logistically complex and creates arisk of psychological harm. However, most machine learning research hasprioritized maximizing effectiveness (i.e., F1 or accuracy score) rather thandata efficiency (i.e., minimizing the amount of data that is annotated). Inthis paper, we use simulated experiments over two datasets at varyingpercentages of abuse to demonstrate that transformers-based active learning isa promising approach to substantially raise efficiency whilst still maintaininghigh effectiveness, especially when abusive content is a smaller percentage ofthe dataset. This approach requires a fraction of labeled data to reachperformance equivalent to training over the full dataset.

Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning