2024
EMNLP
EMNLP 2024
What is the social benefit of hate speech detection research? A Systematic Review
Abstract
AbstractWhile NLP research into hate speech detection has grown exponentially in the last three decades, there has been minimal uptake or engagement from policy makers and non-profit organisations. We argue the absence of ethical frameworks have contributed to this rift between current practice and best practice. By adopting appropriate ethical frameworks, NLP researchers may enable the social impact potential of hate speech research. This position paper is informed by reviewing forty-eight hate speech detection systems associated with thirty-seven publications from different venues.
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The Questioner
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Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary and Machine Learning and Natural Language Processing
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Keyword Pioneer
— policy maker
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Responsible AI
Machine Learning > Application Areas > Fairness
Natural Language Processing > Applications > Text Classification
Interdisciplinary > Social > Social Media Analysis
Natural Language Processing > Applications > Sentiment Analysis
Machine Learning > Learning Types > Fairness