通过联合关键元素提取和分类揭示个人故事中的性骚扰模式
Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization
摘要：
近年来，网上分享的关于性骚扰的个人故事数量呈指数级增长。这在一定程度上受到了\#MeToo和\#TimesUp运动的启发。Safecity是一个在线论坛，供经历或目睹性骚扰的人分享个人经历。到目前为止，它已经收集了\textgreater 10，000个故事。性骚扰发生的情形多种多样，对性骚扰事件进行分类并提取其要素，将对相关方理解和应对性骚扰提供极大帮助。在本研究中，我们在地点、时间和骚扰者特征维度上对这些故事进行了人工标注，并标记了与这些维度相关的关键要素。此外，我们应用自然语言处理技术与联合学习方案，自动分类这些故事在这些方面，并提取关键元素在同一时间。我们还从分类的性骚扰故事中发现了重要的模式。我们相信，我们的注释数据集、拟议算法和分析将以各种方式帮助受到骚扰的人、当局、研究人员和其他相关方，例如自动填写报告，启发公众以防止未来的骚扰，并采取更有效、更快的行动。
英文摘要：
The number of personal stories about sexual harassment shared online has increased exponentially in recent years. This is in part inspired by the \#MeToo and \#TimesUp movements. Safecity is an online forum for people who experienced or witnessed sexual harassment to share their personal experiences. It has collected \textgreater 10,000 stories so far. Sexual harassment occurred in a variety of situations, and categorization of the stories and extraction of their key elements will provide great help for the related parties to understand and address sexual harassment. In this study, we manually annotated those stories with labels in the dimensions of location, time, and harassers' characteristics, and marked the key elements related to these dimensions. Furthermore, we applied natural language processing technologies with joint learning schemes to automatically categorize these stories in those dimensions and extract key elements at the same time. We also uncovered significant patterns from the categorized sexual harassment stories. We believe our annotated data set, proposed algorithms, and analysis will help people who have been harassed, authorities, researchers and other related parties in various ways, such as automatically filling reports, enlightening the public in order to prevent future harassment, and enabling more effective, faster action to be taken.
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。
