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.
Philine Zeinert
Researcher
Philine’s research is on gender bias, semantic annotation, and cross-lingual projection.
Leon Derczynski
Associate professor
My research interests include NLP for misinformation detection and verification, clinical record processing, online harms, and efficient AI.