2019
ACL
ACL 2019
Gender Stereotypes Differ between Male and Female Writings
Abstract
AbstractWritten language often contains gender stereotypes, typically conveyed unintentionally by the author. To study the difference in how female and male authors portray people of different genders, we quantitatively evaluate and analyze the gender stereotypes in their writings on two different datasets and from multiple aspects. We show that writings by females on average have lower gender stereotype scores. We plan to study and interpret the distributions of gender stereotype scores of individual words, and how they differ between male and female writings. We also plan on using more datasets over the past century to study how the stereotypes in female and male writings evolved over time.
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Interdisciplinary Bridge
— Machine Learning and Natural Language Processing
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Keyword Pioneer
— quantitative evaluation
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Hot Topic Early Bird
— natural language
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio