隐藏在众目睽睽之下太久了：使用文本挖掘技术来照亮工作场所的性别歧视和性骚扰
Hidden in Plain Sight For Too Long: Using Text Mining Techniques to Shine a Light on Workplace Sexism and Sexual Harassment
摘要：
目的：这项研究的目的是了解人们如何在工作场所经历性别歧视和性骚扰，通过发现日常性别歧视项目网站everydaysweetm.com上发布的2，362个经验中的主题。方法：采用定量与定性相结合的研究方法。定量方法是一个计算框架，收集和分析大量的工作场所性骚扰的经验。定性的方法是对文本挖掘方法产生的主题进行分析。结果：23个主题编码，然后分组为三个总体主题的性别歧视和性骚扰文学。性别歧视的主题包括女性因性别而受到不利待遇的经历，例如晋升被忽略，机会被剥夺，工资低于男性，以及在会议中被忽视或被谈论。性别歧视和性别骚扰主题包括有关性别歧视和性别骚扰的故事，例如性别歧视的敌意行为，从侮辱和笑话引起厌恶女性的刻板印象到欺凌行为。最后一个主题，不必要的性关注，包含描述性评论和行为用来贬低妇女的故事。不想要的触摸是权重最高的主题，表明网站用户忍受被触摸，拥抱或亲吻，摸索和抓住的普遍程度。结论：这项研究说明了研究人员如何使用自动化过程来超越传统研究方法的限制，并调查互联网上自然发生的大规模数据集，以更好地了解日常工作场所的性别歧视经历。
英文摘要：
Objective: The goal of this study is to understand how people experience sexism and sexual harassment in the workplace by discovering themes in 2,362 experiences posted on the Everyday Sexism Project's website everydaysexism.com. Method: This study used both quantitative and qualitative methods. The quantitative method was a computational framework to collect and analyze a large number of workplace sexual harassment experiences. The qualitative method was the analysis of the topics generated by a text mining method. Results: Twenty-three topics were coded and then grouped into three overarching themes from the sex discrimination and sexual harassment literature. The Sex Discrimination theme included experiences in which women were treated unfavorably due to their sex, such as being passed over for promotion, denied opportunities, paid less than men, and ignored or talked over in meetings. The Sex Discrimination and Gender harassment theme included stories about sex discrimination and gender harassment, such as sexist hostility behaviors ranging from insults and jokes invoking misogynistic stereotypes to bullying behavior. The last theme, Unwanted Sexual Attention, contained stories describing sexual comments and behaviors used to degrade women. Unwanted touching was the highest weighted topic, indicating how common it was for website users to endure being touched, hugged or kissed, groped, and grabbed. Conclusions: This study illustrates how researchers can use automatic processes to go beyond the limits of traditional research methods and investigate naturally occurring large scale datasets on the internet to achieve a better understanding of everyday workplace sexism experiences.
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。
