Subtle Misogyny Detection and Mitigation: An Expert-Annotated Dataset
Brooklyn Sheppard Anna Richter Allison Cohen Elizabeth Allyn Smith Tamara Kneese Carolyne Pelletier Ioana Baldini Yue Dong
Using novel approaches to dataset development, the Biasly dataset captures
the nuance and subtlety of misogyny in ways that are unique within the
literature. Built in collaboration with multi-disciplinary experts and
annotators themselves, the dataset contains annotations of movie subtitles,
capturing colloquial expressions of misogyny in North American film. The
dataset can be used for a range of NLP tasks, including classification,
severity score regression, and text generation for rewrites. In this paper, we
discuss the methodology used, analyze the annotations obtained, and provide
baselines using common NLP algorithms in the context of misogyny detection and
mitigation. We hope this work will promote AI for social good in NLP for bias
detection, explanation, and removal.