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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Annotators' competence in recognizing social influence techniques increases during the annotation process, more pronounced in experts, visibly affecting LLM performance on the resulting data.
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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
Annotators' competence in recognizing social influence techniques increases during the annotation process, more pronounced in experts, visibly affecting LLM performance on the resulting data.