UC Berkeley Measuring Hate Speech Corpus

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.