dataset
UC Berkeley Measuring Hate Speech Corpus
datasetactiveprovisional
uc-berkeley-measuring-hate-speech-corpus-7537b9c3·1 events·first seen 21d agoAliases: UC Berkeley Measuring Hate Speech Corpus
Co-occurring entities
More like this (12)
From Self to Other: Evaluating Demographic Perspective-Taking in LLM Hate Speech Annotationhate speech detectionReeve Foundation Multilingual Corpushate-based rhetoricpublic consultation corpus (U.S. AI Action Plan)Measuring Human Value Expression in Social Media Texts: Calibrated LLM Annotation and Encoder TransferBerlin Database of Emotional Speech (EMO-DB)Massive Text Embedding BenchmarkKomi-Yazva–Russian Parallel CorpusUniversity of California, BerkeleyVUA Metaphor CorpusAG-MG Parallel Corpus
Recent events (1)
Interaction SSD: Modeling Annotator Identity Effects on Hate Speech Semantic Gradients
This paper introduces Interaction SSD, an extension of Supervised Semantic Differential that tests how semantic meaning varies across moderating variables such as annotator group identity. Applied to the UC Berkeley Measuring Hate Speech corpus, the method detects that annotator racial identity significantly moderates hate-speech judgments, with a shared gradient distinguishing dehumanizing hostility from counter-speech and an interaction gradient revealing group-linked differences in predictive semantic cues. The approach makes moderated meaning-outcome relationships statistically testable and interpretable through standard SSD tooling.