Researchers Develop Continuous Hate Speech Measurement System Using Deep Learning and Item Response Theory
Researchers created a system that measures hate speech on a continuous spectrum from genocidal to supportive speech, rather than using binary classifications. The approach combines deep learning with Rasch item response theory and accounts for individual annotator perspectives across 50,070 social media comments. The method offers a more nuanced framework for hate speech detection with built-in explainability for automated predictions.
A research team has proposed a novel approach to hate speech detection that treats the phenomenon as a continuous, interval-valued spectrum rather than a binary classification. The system decomposes hate speech into 10 ordinal labels that are then reconstituted through probabilistic latent modeling while adjusting for each annotator's individual labeling perspective. The researchers applied their RoBERTa-based deep learning model to a dataset of 50,070 comments from YouTube, Twitter, and Reddit, annotated by over 11,000 Amazon Mechanical Turk workers. The multitask architecture integrates explainability by design, allowing users to understand how the continuous score is derived from component parts. The model demonstrated improved accuracy compared to alternative approaches, suggesting that continuous measurement frameworks may better capture the nuanced nature of hate speech compared to traditional binary systems.
What's missing
The paper does not discuss potential limitations of crowdsourced annotation quality, inter-annotator agreement metrics, or how the system performs on hate speech in languages other than English. Additionally, the generalizability of findings beyond the specific social media platforms studied (YouTube, Twitter, Reddit) and potential demographic biases in the annotator pool are not addressed in the abstract.
What different sources said
- arXiv cs.LGCenter
Measuring a hate speech spectrum with faceted Rasch item response theory and perspective-aware, explainable-by-design deep learning
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