美国的#MeToo(我也是)运动卡塔尔世界杯8强波胆分析早期Twitter对话文本分析%A Modrek,Sepideh %A Chakalov,Bozhidar %+旧金山州立大学健康公平研究所，1600 Hollaway Avenue HSS 386, San Francisco, CA, 94132, usa, 1 415 405 7556, smodrek@sfsu.edu %K社交媒体%K性虐待%K性侵犯%K机器学习%K信息流行病学%K信息监控%D 2019 %7 03.09.2019 %9原文%J J Med Internet Res %G English %X背景:自2017年10月发起以来，#MeToo运动引发了一场关于性骚扰、性虐待和性侵犯的国际辩论，并朝着多个方向发展。早期的讨论大多发生在Twitter等公共社交媒体网站上，标签运动就是在那里开始的。目的:本研究的目的是记录、描述和量化来自美国Twitter数据的#MeToo运动的早期公共话语和对话。我们关注的是公开披露性侵犯/性虐待以及此类事件的早期生活经历的帖子。方法:我们在2017年10月14日至21日(即运动的第一周)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(N= 11935)的带有“MeToo”短语的新颖英语推文的内容。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，对揭露性侵和性虐事件以及性侵和性虐早期生活经历的个别推文内容进行总结和分类。结果:我们发现，最具预测性的词语创造了性侵犯和性虐待揭露的生动原型。 We then estimated that in the first week of the movement, 11% of novel English language tweets with the words “MeToo” revealed details about the poster’s experience of sexual assault or abuse and 5.8% revealed early life experiences of such events. 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. %M 31482849 %R 10.2196/13837 %U //www.mybigtv.com/2019/9/e13837/ %U https://doi.org/10.2196/13837 %U http://www.ncbi.nlm.nih.gov/pubmed/31482849