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This research introduces a novel framework that combines deep learning with Rasch item response theory to measure hate speech on a continuous spectrum rather than a binary classification. By decomposing the concept into ten ordinal labels and adjusting for annotator perspectives, the system offers improved accuracy and design-based explainability for complex NLP tasks. The approach represents a significant methodological shift toward modeling nuanced social constructs with probabilistic latent modeling in supervised learning architectures.
Measuring a hate speech spectrum with faceted Rasch item response theory and perspective-aware, explainable-by-design deep learning
arXiv:2009.10277v2 Announce Type: replace-cross Abstract: We propose a system for measuring hate speech on a continuous, interval-valued spectrum ranging from genocidal to supportive speech by combining supervised deep learning with faceted Rasch item response theory (IRT). We decompose the theoretical construct of hate speech into constituent concepts operationalized as 10 ordinal labels. Those labels are reconstituted via IRT probabilistic latent modeling into an interval outcome measure while simultaneously estimating and adjusting for each annotator's labeling perspective. Our scaling procedure integrates naturally with a multitask deep learning architecture for automated prediction, allowing design-based explainability of the continuous score through those components. We apply this method to a new, open source dataset of 50,070 social media comments sourced from YouTube, Twitter, and Reddit, annotated and labeled by 11,143 United States-based Amazon Mechanical Turk workers. Our RoBERTa-based model shows improved accuracy compared to alternative approaches. This system offers a new paradigm for supervised NLP that encourages continuous rather than binary constructs, and design-based incorporation of annotator perspective and model explainability.
Abstract:We propose a system for measuring hate speech on a continuous, interval-valued spectrum ranging from genocidal to supportive speech by combining supervised deep learning with faceted Rasch item response theory (IRT). We decompose the theoretical construct of hate speech into constituent concepts operationalized as 10 ordinal labels. Those labels are reconstituted via IRT probabilistic latent modeling into an interval outcome measure while simultaneously estimating and adjusting for each annotator's labeling perspective. Our scaling procedure integrates naturally with a multitask deep learning architecture for automated prediction, allowing design-based explainability of the continuous score through those components. We apply this method to a new, open source dataset of 50,070 social media comments sourced from YouTube, Twitter, and Reddit, annotated and labeled by 11,143 United States-based Amazon Mechanical Turk workers. Our RoBERTa-based model shows improved accuracy compared to alternative approaches. This system offers a new paradigm for supervised NLP that encourages continuous rather than binary constructs, and design-based incorporation of annotator perspective and model explainability.
Computation and Language (cs.CL); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
arXiv:2009.10277 [cs.CL]
(or arXiv:2009.10277v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2009.10277 Focus to learn more arXiv-issued DOI via DataCite
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