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With the recent rise of #MeToo, an increasing number of personal storiesabout sexual harassment and sexual abuse have been shared online. In order topush forward the fight against such harassment and abuse, we present the taskof automatically categorizing and analyzing various forms of sexual harassment,based on stories shared on the online forum SafeCity. For the labels ofgroping, ogling, and commenting, our single-label CNN-RNN model achieves anaccuracy of 86.5%, and our multi-label model achieves a Hamming score of 82.5%.Furthermore, we present analysis using LIME, first-derivative saliencyheatmaps, activation clustering, and embedding visualization to interpretneural model predictions and demonstrate how this extracts features that canhelp automatically fill out incident reports, identify unsafe areas, avoidunsafe practices, and 'pin the creeps'.

SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories