Analyzing Hate Speech Data Along Racial, Gender and Intersectional Axes

MCML Authors

Antonis Maronikolakis

* Former Member

→ Group Hinrich Schütze
 Computational Linguistics

Hinrich Schütze

Prof. Dr.

Core PI

Computational Linguistics

Abstract

To tackle the rising phenomenon of hate speech, efforts have been made towards data curation and analysis. When it comes to analysis of bias, previous work has focused predominantly on race. In our work, we further investigate bias in hate speech datasets along racial, gender and intersectional axes. We identify strong bias against African American English (AAE), masculine and AAE+Masculine tweets, which are annotated as disproportionately more hateful and offensive than from other demographics. We provide evidence that BERT-based models propagate this bias and show that balancing the training data for these protected attributes can lead to fairer models with regards to gender, but not race.

inproceedings MBS22

---

GeBNLP 2022

4th Workshop on Gender Bias in Natural Language Processing. Seattle, WA, USA, Jul 15, 2022.

---

Authors

A. Maronikolakis • P. Baader • H. Schütze

---

Links

DOI

---

Research Area

 B2 | Natural Language Processing