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