Abstract

Sexism, a form of oppression based on one’s sex, manifests itself in numerous ways and causes enormous suffering. In view of the growing number of experiences of sexism reported online, categorizing these recollections automatically can assist the fight against sexism, as it can facilitate effective analyses by gender studies researchers and government officials involved in policy making. In this paper, we investigate the fine-grained, multi-label classification of accounts (reports) of sexism. To the best of our knowledge, we work with considerably more categories of sexism than any published work through our 23-class problem formulation. Moreover, we propose a multi-task approach for fine-grained multi-label sexism classification that leverages several supporting tasks without incurring any manual labeling cost. Unlabeled accounts of sexism are utilized through unsupervised learning to help construct our multi-task setup. We also devise objective functions that exploit label correlations in the training data explicitly. Multiple proposed methods outperform the state-of-the-art for multi-label sexism classification on a recently released dataset across five standard metrics.

Highlights

  • Sexism, defined as prejudice, stereotyping, or discrimination based on a person’s sex, occurs in various overt and subtle forms, permeating personal as well as professional spaces

  • Traditional Machine Learning (TML): We report the performance using Support Vector Machine (SVM), Logistic Regression (LR), and Random Forests (RF), each applied on two feature sets, namely the average of the ELMo vectors for a post’s words and TF-IDF on word unigrams and bigrams

  • For each deep learning method, for each metric, the mean of the results obtained over three runs is given

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Summary

Introduction

Sexism, defined as prejudice, stereotyping, or discrimination based on a person’s sex, occurs in various overt and subtle forms, permeating personal as well as professional spaces. With increasingly many people sharing recollections of sexism experienced or witnessed by them, the automatic classification of these accounts into well-conceived categories of sexism can help fight this oppression, as it can better equip authorities formulating policies and researchers of gender studies to analyze sexism. The detection of sexism differs from and can complement the classification of sexism. We observe the distinction between sexist statements (e.g., posts whereby one perpetrates sexism) and the accounts of sexism suffered or witnessed (e.g., personal recollections shared as part of the #metoo movement). We note the prior work on detecting or classifying personal stories of sexual harassment and/or assault (Chowdhury et al, 2019; Karlekar and Bansal, 2018). We focus on classifying an account (report) of sexism involving any set of categories of sexism

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