Automated Hate Speech Detection and the Problem of Offensive Language¶
Davidson. (2017). Automated Hate Speech Detection and the Problem of Offensive Language.
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Domain-specific¶
- Label Ambiguity
- and threshold-region radiology reads, where the label reflects an adjudicator's judgement near a clinical boundary. Hiring and risk analytics — "qualified" versus "not qualified" and "high risk" versus "moderate," where the label encodes the assessor's interpretation of ambiguous cases. Content moderation — "hate speech," "misinformation," and "harassment," where boundary cases drive most inter-annotator disagreement and most of the model's apparent failures
This sourceA multi-class study showing that separating hate speech from merely offensive language is where classification is hardest, with almost 40% of hate speech misclassified.
Supported in partVerified against the work's full text
“Close analysis of the predictions and the errors shows when we can reliably separate hate speech from other offensive language and when this differentiation is more difficult.”
- and threshold-region radiology reads, where the label reflects an adjudicator's judgement near a clinical boundary. Hiring and risk analytics — "qualified" versus "not qualified" and "high risk" versus "moderate," where the label encodes the assessor's interpretation of ambiguous cases. Content moderation — "hate speech," "misinformation," and "harassment," where boundary cases drive most inter-annotator disagreement and most of the model's apparent failures
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