MHS

When Does Demographic Information Help? Data and Modeling Regimes for Perspective-Aware Hate Speech Detection

This paper investigates when demographic features improve hate speech detection models that account for annotator perspectives, finding that gains are not universal but depend on specific data and modeling conditions. The authors identify that demographic information helps most in regimes with low training disagreement, high test disagreement, sufficient training data, and strong demographic overlap between train and test sets. They introduce a gated demographic residual model that selectively applies demographic adjustments to text-only predictions, demonstrating effectiveness on high-disagreement and low-confidence examples using the MHS and POPQUORN datasets. The work cautions against assuming demographic features are universally beneficial in subjective NLP tasks.