This research addresses the critical challenge of annotator disagreement in hate speech detection systems, providing frameworks to improve classification accuracy and reliability.

• Develops methodologies to handle subjective interpretations of hate speech among different annotators
• Proposes techniques to incorporate disagreement signals into machine learning models
• Demonstrates improved performance by accounting for annotator diversity rather than forcing consensus
• Establishes a more nuanced approach to content classification that reflects real-world complexity

For security teams, this research offers practical pathways to build more robust content moderation systems that can better navigate the subjective nature of harmful content detection, reducing both false positives and negatives in automated filtering.

Dealing with Annotator Disagreement in Hate Speech Classification