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Examining Racial Bias in an Online Abuse Corpus with Structural Topic Modeling by Davidson, Thomas; Bhattacharya, Debasmita is a scholarly article available to read on EtoBox.

What is Examining Racial Bias in an Online Abuse Corpus with Structural Topic Modeling about?

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.

Author

Davidson, Thomas; Bhattacharya, Debasmita

Published

2020

Language

EN