Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization

TL;DR

This paper presents a joint learning approach to automatically categorize sexual harassment stories and extract key elements, aiding understanding and prevention efforts based on a large annotated dataset.

Contribution

It introduces a novel joint learning scheme for categorizing stories and extracting key elements, along with a comprehensive annotated dataset of sexual harassment stories.

Findings

Effective automatic categorization of stories in location, time, and harassers' characteristics.

Successful extraction of key elements related to sexual harassment stories.

Uncovered significant patterns in the categorized data.

Abstract

The number of personal stories about sexual harassment shared online has increased exponentially in recent years. This is in part inspired by the \#MeToo and \#TimesUp movements. Safecity is an online forum for people who experienced or witnessed sexual harassment to share their personal experiences. It has collected \textgreater 10,000 stories so far. Sexual harassment occurred in a variety of situations, and categorization of the stories and extraction of their key elements will provide great help for the related parties to understand and address sexual harassment. In this study, we manually annotated those stories with labels in the dimensions of location, time, and harassers' characteristics, and marked the key elements related to these dimensions. Furthermore, we applied natural language processing technologies with joint learning schemes to automatically categorize these stories in those dimensions and extract key elements at the same time. We also uncovered significant patterns from the categorized sexual harassment stories. We believe our annotated data set, proposed algorithms, and analysis will help people who have been harassed, authorities, researchers and other related parties in various ways, such as automatically filling reports, enlightening the public in order to prevent future harassment, and enabling more effective, faster action to be taken.