Large-scale statistical analysis of four harmful language datasets reveals that interactions between annotator characteristics and linguistic cues drive annotation variation, with lexical features and attitudes prominent but patterns varying by dataset.
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2 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
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cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Doing social-influence annotation measurably raises annotator competence and confidence, more so for experts, and those shifts alter LLM performance on the resulting labels.
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Who and What? Using Linguistic Features and Annotator Characteristics to Analyze Annotation Variation
Large-scale statistical analysis of four harmful language datasets reveals that interactions between annotator characteristics and linguistic cues drive annotation variation, with lexical features and attitudes prominent but patterns varying by dataset.
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How Annotation Trains Annotators: Competence Development in Social Influence Recognition
Doing social-influence annotation measurably raises annotator competence and confidence, more so for experts, and those shifts alter LLM performance on the resulting labels.