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Annotating Online Misogyny
Philine Zeinert, Nanna Inie, and Leon Derczynski have created a powerful tool to identify and combat misogyny online.
Abstract:
Online misogyny, a category of online abusive language, has serious and harmful social consequences. Automatic detection of misogynistic language online, while imperative, poses
complicated challenges to both data gathering, data annotation, and bias mitigation, as this
type of data is linguistically complex and diverse. This paper makes three contributions
in this area: Firstly, we describe the detailed design of our iterative annotation process and
codebook. Secondly, we present a comprehensive taxonomy of labels for annotating misogyny in natural written language, and finally, we introduce a high-quality dataset of annotated posts sampled from social media posts.
For more information:
https://www.derczynski.com/papers/annotating_online_misogyny.pdf
https://www.derczynski.com/papers/annotating_online_misogyny.pdf
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By IT University of CopenhagenPhiline Zeinert, Nanna Inie, and Leon Derczynski have created a powerful tool to identify and combat misogyny online.
Abstract:
Online misogyny, a category of online abusive language, has serious and harmful social consequences. Automatic detection of misogynistic language online, while imperative, poses
complicated challenges to both data gathering, data annotation, and bias mitigation, as this
type of data is linguistically complex and diverse. This paper makes three contributions
in this area: Firstly, we describe the detailed design of our iterative annotation process and
codebook. Secondly, we present a comprehensive taxonomy of labels for annotating misogyny in natural written language, and finally, we introduce a high-quality dataset of annotated posts sampled from social media posts.
For more information:
https://www.derczynski.com/papers/annotating_online_misogyny.pdf
https://www.derczynski.com/papers/annotating_online_misogyny.pdf