Hate Speech

Fritz_X_DargesBlue42… Who Are You?

January 14, 2025
by Jonathan Pérez. Reflecting on the complexities of the human experience is paramount to conducting research. Jonathan Pérez, through his exploration of a conspiracy subreddit, reflects on his experience trying to find the human behind the datum. Jonathan critiques the harmful effects of dehumanizing rhetoric and the researcher’s responsibility to navigate ethical implications. In doing so, he establishes three guiding rules to support researchers seeking to humanize their analysis: 1) a researcher must always find the story behind the data; 2) a researcher must protect themselves; 3) a researcher must still humanize participants (even those who perpetuate harmful narratives).

Claudia von Vacano, Ph.D.

Founding Executive Director, P.I., Research Director, FSRDC

Dr. Claudia von Vacano is the Founding Executive Director and Senior Research Associate of D-Lab and Digital Humanities at Berkeley and is on the boards of the Social Science Matrix and Berkeley Center for New Media. She has worked in policy and educational administration since 2000, and at the UC Office of the President and UC Berkeley since 2008. She received a Master’s degree from Stanford University in Learning, Design, and Technology. Her doctorate is in Policy, Organizations, Measurement, and Evaluation from UC Berkeley. Her expertise is in organizational theory and...

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

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...