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

Domaine:

natural language processing

Type de record:

paper
Créateur:
DavBha
Éditeur:
arXiv
Hôte:avatar
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. Please cite the published version, see proceedings of ICWSM 2020

Visit

doi.orgarxiv.org

Tasks

hate speech detectiontext classification

Tags

Computation and Language (cs.CL)Social and Information Networks (cs.SI)FOS: Computer and information sciencesFOS: Computer and information sciences

Licenses

arXiv.org perpetual, non-exclusive licensehttp://arxiv.org/licenses/nonexclusive-distrib/1.0/

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