Examining inferred author and textual correlates of harmful language annotation.

This study examines whether the psycholinguistic and demographic characteristics of authors of online texts are correlated with the way harmful language, such as toxicity and hate speech, is judged. We apply artificial intelligence models to two harmful language datasets, Jigsaw's Special Rater Pool...

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Detalles Bibliográficos

Publicado en:	Language Resources & Evaluation Vol. 59; no. 4; pp. 3411 - 3443
Autores principales:	Korre, Katerina, Yenikent, Seren, Basile, Angelo, Spallaccia, Beatrice, Franco-Salvador, Marc, Barrón-Cedeño, Alberto
Formato:	Artículo
Publicado:	Springer Nature Dec2025
Materias:	Hate speech Psycholinguistics Toxicity testing Discriminatory language Content analysis Sociocultural factors Text mining Machine learning
Acceso en línea:	Ver este registro en EBSCOhost