Gender Stereotypes Differ between Male and Female Writings

By Yusu Qian. 2019

6 citations recorded by the source corpus.

Written 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.

Subfields: Artificial Intelligence, Gender Studies, Natural Language Processing, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Gender Studies

Adina Williams

In Natural Language Processing

In Safety, Robustness & Interpretability

Citations in the corpus

Queens Are Powerful Too: Mitigating Gender Bias in Dialogue Generation (2019, background): “…2019; Emami et al. 2019; Garimella et al. 2019; Gaut et al. 2020; Habash et al. 2019; Hashempour 2019; Hoyle et al. 2019; Lee et al. 2019a; Lepp 2019; Qian 2019; Qian et al. 2019; Shariﬁrad et al. 2019; Shariﬁrad and Matwin 2019; Stanovsky et al. 2019; O’Neil 2016; Blodgett et al. 2020; Nangia et…”
Multi-Dimensional Gender Bias Classification (2020, background): “More specifically, automatic gender attribution has reported many successes (Koppel et al., 2002; Koolen and van Cranenburgh, 2017; Qian, 2019), often driven by the fact that authors of specific genders tend to prefer producing content about topics that belie those gender (Sarawgi et al.”