Papers with MeToo
Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization (D19-1)
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| Challenge: | Sexual harassment is a pervasive, worldwide problem with a long history . statistics show that girls and women are put at high risk of experiencing harassment. |
| Approach: | They manually annotated sexual harassment stories with labels in dimensions of location, time, and harassers’ characteristics and applied natural language processing techniques to extract key elements at the same time. |
| Outcome: | The proposed algorithms will help people who have been harassed, authorities, researchers and other related parties in various ways, such as automatically filling reports, and enabling faster action to be taken. |
SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories (D18-1)
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| Challenge: | With the recent rise of #MeToo, an increasing number of personal stories about sexual harassment and sexual abuse have been shared online. |
| Approach: | They propose to use CNN-RNN model to automatically categorize and analyze sexual harassment data from SafeCity forums. |
| Outcome: | The proposed model achieves an accuracy of 86.5% for groping, ogling, and commenting, and 82.5% in multi-label models. |