@inproceedings{kurrek-etal-2020-towards,
title = "Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage",
author = "Kurrek, Jana and
Saleem, Haji Mohammad and
Ruths, Derek",
editor = "Akiwowo, Seyi and
Vidgen, Bertie and
Prabhakaran, Vinodkumar and
Waseem, Zeerak",
booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.alw-1.17/",
doi = "10.18653/v1/2020.alw-1.17",
pages = "138--149",
abstract = "Abusive language classifiers have been shown to exhibit bias against women and racial minorities. Since these models are trained on data that is collected using keywords, they tend to exhibit a high sensitivity towards pejoratives. As a result, comments written by victims of abuse are frequently labelled as hateful, even if they discuss or reclaim slurs. Any attempt to address bias in keyword-based corpora requires a better understanding of pejorative language, as well as an equitable representation of targeted users in data collection. We make two main contributions to this end. First, we provide an annotation guide that outlines 4 main categories of online slur usage, which we further divide into a total of 12 sub-categories. Second, we present a publicly available corpus based on our taxonomy, with 39.8k human annotated comments extracted from Reddit. This corpus was annotated by a diverse cohort of coders, with Shannon equitability indices of 0.90, 0.92, and 0.87 across sexuality, ethnicity, and gender. Taken together, our taxonomy and corpus allow researchers to evaluate classifiers on a wider range of speech containing slurs."
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<abstract>Abusive language classifiers have been shown to exhibit bias against women and racial minorities. Since these models are trained on data that is collected using keywords, they tend to exhibit a high sensitivity towards pejoratives. As a result, comments written by victims of abuse are frequently labelled as hateful, even if they discuss or reclaim slurs. Any attempt to address bias in keyword-based corpora requires a better understanding of pejorative language, as well as an equitable representation of targeted users in data collection. We make two main contributions to this end. First, we provide an annotation guide that outlines 4 main categories of online slur usage, which we further divide into a total of 12 sub-categories. Second, we present a publicly available corpus based on our taxonomy, with 39.8k human annotated comments extracted from Reddit. This corpus was annotated by a diverse cohort of coders, with Shannon equitability indices of 0.90, 0.92, and 0.87 across sexuality, ethnicity, and gender. Taken together, our taxonomy and corpus allow researchers to evaluate classifiers on a wider range of speech containing slurs.</abstract>
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%0 Conference Proceedings
%T Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage
%A Kurrek, Jana
%A Saleem, Haji Mohammad
%A Ruths, Derek
%Y Akiwowo, Seyi
%Y Vidgen, Bertie
%Y Prabhakaran, Vinodkumar
%Y Waseem, Zeerak
%S Proceedings of the Fourth Workshop on Online Abuse and Harms
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F kurrek-etal-2020-towards
%X Abusive language classifiers have been shown to exhibit bias against women and racial minorities. Since these models are trained on data that is collected using keywords, they tend to exhibit a high sensitivity towards pejoratives. As a result, comments written by victims of abuse are frequently labelled as hateful, even if they discuss or reclaim slurs. Any attempt to address bias in keyword-based corpora requires a better understanding of pejorative language, as well as an equitable representation of targeted users in data collection. We make two main contributions to this end. First, we provide an annotation guide that outlines 4 main categories of online slur usage, which we further divide into a total of 12 sub-categories. Second, we present a publicly available corpus based on our taxonomy, with 39.8k human annotated comments extracted from Reddit. This corpus was annotated by a diverse cohort of coders, with Shannon equitability indices of 0.90, 0.92, and 0.87 across sexuality, ethnicity, and gender. Taken together, our taxonomy and corpus allow researchers to evaluate classifiers on a wider range of speech containing slurs.
%R 10.18653/v1/2020.alw-1.17
%U https://aclanthology.org/2020.alw-1.17/
%U https://doi.org/10.18653/v1/2020.alw-1.17
%P 138-149
Markdown (Informal)
[Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage](https://aclanthology.org/2020.alw-1.17/) (Kurrek et al., ALW 2020)
ACL