SafeCity：了解各种形式的性骚扰个人故事
SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories
摘要：
随着最近#MeToo的兴起，越来越多关于性骚扰和性虐待的个人故事在网上分享。为了推动打击这种骚扰和虐待的斗争，我们提出了一项任务，根据在线论坛SafeCity上分享的故事，自动分类和分析各种形式的性骚扰。对于摸索，盯着看和评论的标签，我们的单标签CNN-RNN模型达到了86.5%的准确率，我们的多标签模型达到了82.5%的汉明得分。此外，我们使用LIME、一阶导数显着性热图、激活聚类和嵌入可视化来解释神经模型预测，并演示如何提取有助于自动填写事件报告、识别不安全区域、避免不安全做法和“固定蠕变”的特征。
英文摘要：
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'.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
