Sexual Harassment Story Classification and Key Information Identification

Yingchi Liu, Quanzhi Li, Xiaozhong Liu, Qiong Zhang, Luo Si

Abstract: Recently more and more personal stories about sexual harassment are shared online, mainly inspired by the \#MeToo movement. Safecity is an online forum for victims of sexual harassment to share their personal experience. Previous study applied neural network models to classify the harassment forms of the stories. To uncover patterns of sexual harassment, the extraction of the key elements and the categorization of these stories in different dimensions can be useful as well. In this study, we proposed neural network models to extract key elements including harasser, time, location and trigger words. In addition, we categorized these stories from different dimensions, such as location, time, and harassers' characteristics, including their age range, single/multiple harassers, profession, and relationship with the victims. We further demonstrated that encoding the key element information in the story categorization model can improve its performance. The proposed approaches and analysis would be helpful in automatically filing reports, raising public awareness, making preventing strategies and etc.