TY - JOUR AU - Modrek, Sepideh AU - Chakalov, Bozhidar PY - 2019 DA - 2019/09/03 TI -美国的#MeToo运动:早期推特对话的文本分析JO - J Med Internet Res SP - e13837 VL - 21 IS - 9 KW -社交媒体KW -性虐待KW -性侵犯KW -机器学习KW -信息病学KW -信息监控AB -背景:#MeToo运动自2017年10月发起以来，引发了一场关于性骚扰、性侵和性侵犯的国际大讨论，并向多个方向发展。早期的大部分对话都发生在Twitter等公共社交媒体网站上，这也是标签运动的发源地。目的:本研究的目的是通过美国推特数据记录、描述和量化#MeToo运动的早期公共话语和对话。我们专注于公开的第一人称揭露性侵犯/性虐待的帖子，以及此类事件的早期生活经历。方法:我们在2017年10月14日至21日(即运动的第一周)期间从Twitter Premium应用程序编程界面购买了完整的推文和相关元数据。我们研究了来自美国境内带有“MeToo”短语的新颖英语推文的内容(N=11,935)。我们使用机器学习方法、最小绝对收缩和选择算子回归以及支持向量机模型，对揭露性侵和性虐待以及性侵和性虐待早期生活经历的单个推文内容进行总结和分类。结果:我们发现，最具预言性的词语创造了性侵犯和性虐待揭露的生动原型。然后我们估计，在运动的第一周，带有“MeToo”字样的新奇英语推文中，11%透露了发帖者遭受性侵犯或性虐待的细节，5.8%透露了此类事件的早期生活经历。 We examined the demographic composition of posters of sexual assault and abuse and found that white women aged 25-50 years were overrepresented in terms of their representation on Twitter. Furthermore, we found that the mass sharing of personal experiences of sexual assault and abuse had a large reach, where 6 to 34 million Twitter users may have seen such first-person revelations from someone they followed in the first week of the movement. Conclusions: These data illustrate that revelations shared went beyond acknowledgement of having experienced sexual harassment and often included vivid and traumatic descriptions of early life experiences of assault and abuse. These findings and methods underscore the value of content analysis, supported by novel machine learning methods, to improve our understanding of how widespread the revelations were, which likely amplified the spread and saliency of the #MeToo movement. SN - 1438-8871 UR - //www.mybigtv.com/2019/9/e13837/ UR - https://doi.org/10.2196/13837 UR - http://www.ncbi.nlm.nih.gov/pubmed/31482849 DO - 10.2196/13837 ID - info:doi/10.2196/13837 ER -