# SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories

arXiv: https://arxiv.org/abs/1809.04739 · HTML: https://mlsanity.com/paper/1809.04739/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [CRADLE Bench: A Clinician-Annotated Benchmark for Multi-Faceted Mental Health Crisis and Safety Risk Detection](https://mlsanity.com/paper/2510.23845/) (2025)
- [Extracting Incidents, Effects, and Requested Advice from MeToo Posts](https://mlsanity.com/paper/2303.10573/) (2023)
- [TWeddit : A Dataset of Triggering Stories Predominantly Shared by Women on Reddit](https://mlsanity.com/paper/2601.11819/) (2026)

## Abstract (arXiv)

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'.