Automatic Detection of Sexist Statements Commonly Used at the Workplace

TL;DR

This paper introduces a new dataset and deep learning model for detecting subtle workplace sexism, outperforming previous Twitter-based models with an F1 score of 0.88.

Contribution

It presents a novel dataset of workplace-appropriate sexist statements and a deep learning approach that improves detection accuracy over prior social media-focused models.

Findings

Achieved an F1 score of 0.88 on workplace sexism detection

Deep learning model outperforms previous Twitter-based models

Uses LSTMs with attention mechanisms and word embeddings

Abstract

Detecting hate speech in the workplace is a unique classification task, as the underlying social context implies a subtler version of conventional hate speech. Applications regarding a state-of the-art workplace sexism detection model include aids for Human Resources departments, AI chatbots and sentiment analysis. Most existing hate speech detection methods, although robust and accurate, focus on hate speech found on social media, specifically Twitter. The context of social media is much more anonymous than the workplace, therefore it tends to lend itself to more aggressive and "hostile" versions of sexism. Therefore, datasets with large amounts of "hostile" sexism have a slightly easier detection task since "hostile" sexist statements can hinge on a couple words that, regardless of context, tip the model off that a statement is sexist. In this paper we present a dataset of sexist statements that are more likely to be said in the workplace as well as a deep learning model that can achieve state-of-the art results. Previous research has created state-of-the-art models to distinguish "hostile" and "benevolent" sexism based simply on aggregated Twitter data. Our deep learning methods, initialized with GloVe or random word embeddings, use LSTMs with attention mechanisms to outperform those models on a more diverse, filtered dataset that is more targeted towards workplace sexism, leading to an F1 score of 0.88.