Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets
Nedjma Ousidhoum, Yangqiu Song, Dit‐Yan Yeung
Abstract
Work on bias in hate speech typically aims to improve classification performance while relatively overlooking the quality of the data. We examine selection bias in hate speech in a language and label independent fashion. We first use topic models to discover latent semantics in eleven hate speech corpora, then, we present two bias evaluation metrics based on the semantic similarity between topics and search words frequently used to build corpora. We discuss the possibility of revising the data collection process by comparing datasets and analyzing contrastive case studies.
Topics & Concepts
Computer scienceSelection (genetic algorithm)Natural language processingSemantics (computer science)Artificial intelligenceLatent semantic analysisProcess (computing)Selection biasSimilarity (geometry)Quality (philosophy)Programming languagePathologyMedicinePhilosophyOperating systemImage (mathematics)EpistemologyHate Speech and Cyberbullying Detection