Hate Speech

Hate Speech

The hate speech measurement project began in early 2017 at UC Berkeley’s D-Lab. Our research project applies data science techniques such as machine learning to track changes in hate speech over time and across social media platforms. After three years, we have now published our groundbreaking method that measures hate speech with precision while mitigating the influence of human bias. Read the manuscript here.

Abstract: Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application

November 26, 2020
Chris J. Kennedy

We propose a general method for measuring complex variables on a continuous, interval spectrum by combining supervised deep learning with the Constructing Measures approach to faceted Rasch item response theory (IRT). We decompose the target construct, hate speech in our case, into multiple constituent components that are labeled as ordinal survey items. Those survey responses are transformed via an IRT nonlinear activation into a debiased, continuous outcome measure. Our method estimates the survey interpretation bias of the human labelers and eliminates that influence on the generated...