# Examining Racial Bias in an Online Abuse Corpus with Structural Topic Modeling

arXiv: https://arxiv.org/abs/2005.13041 · HTML: https://mlsanity.com/paper/2005.13041/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 3 of 3)

- [Handling Bias in Toxic Speech Detection: A Survey](https://mlsanity.com/paper/2202.00126/) (2022)
- [Examining Temporal Bias in Abusive Language Detection](https://mlsanity.com/paper/2309.14146/) (2023)
- [Towards a Deep Multi-layered Dialectal Language Analysis: A Case Study of African-American English](https://mlsanity.com/paper/2206.08978/) (2022)

## Abstract (arXiv)

We use structural topic modeling to examine racial bias in data collected to train models to detect hate speech and abusive language in social media posts. We augment the abusive language dataset by adding an additional feature indicating the predicted probability of the tweet being written in African-American English. We then use structural topic modeling to examine the content of the tweets and how the prevalence of different topics is related to both abusiveness annotation and dialect prediction. We find that certain topics are disproportionately racialized and considered abusive. We discuss how topic modeling may be a useful approach for identifying bias in annotated data.