Quantifying and Predicting Disagreement in Graded Human Ratings

Abstract

It is increasingly recognized that humans do not always agree, and disagreement is inherent in many annotation tasks. However, not all items in a given task elicit the same level of opinion divergence. In this paper, we study the extent to which item-level annotation variation and variation structure can be captured from text features, focusing on inappropriate language detection, including offensive language, hate speech, and toxic language detection. We model annotation variation to assess whether the degree of annotation divergence can be predicted from item-level textual features. We also propose the Opposition Index, a metric that quantifies the extent of opposing stances among annotators based on their Likert ratings.

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