Modrek Sepideh, Chakalov Bozhidar
Health Equity Institute, San Francisco State University, San Francisco, CA, United States.
Economics Department, San Francisco State University, San Francisco, CA, United States.
J Med Internet Res. 2019 Sep 3;21(9):e13837. doi: 10.2196/13837.
The #MeToo movement sparked an international debate on the sexual harassment, abuse, and assault and has taken many directions since its inception in October of 2017. Much of the early conversation took place on public social media sites such as Twitter, where the hashtag movement began.
The aim of this study is to document, characterize, and quantify early public discourse and conversation of the #MeToo movement from Twitter data in the United States. We focus on posts with public first-person revelations of sexual assault/abuse and early life experiences of such events.
We purchased full tweets and associated metadata from the Twitter Premium application programming interface between October 14 and 21, 2017 (ie, the first week of the movement). We examined the content of novel English language tweets with the phrase "MeToo" from within the United States (N=11,935). We used machine learning methods, least absolute shrinkage and selection operator regression, and support vector machine models to summarize and classify the content of individual tweets with revelations of sexual assault and abuse and early life experiences of sexual assault and abuse.
We found that the most predictive words created a vivid archetype of the revelations of sexual assault and abuse. 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.
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.
“MeToo”运动引发了一场关于性骚扰、虐待和性侵的国际辩论，自2017年10月发起以来呈现出多种发展态势。早期的许多讨论在推特等公共社交媒体网站上展开，该话题标签运动正是从推特开始的。
本研究旨在记录、描述和量化美国推特数据中“MeToo”运动早期的公众话语和讨论。我们关注那些以第一人称公开揭露性侵/虐待以及此类事件早期生活经历的推文。
我们从推特高级应用程序编程接口购买了2017年10月14日至21日（即该运动的第一周）的完整推文及相关元数据。我们研究了美国境内包含“MeToo”短语的全新英文推文内容（N = 11935条）。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，对那些揭露性侵和虐待以及此类事件早期生活经历的单条推文内容进行总结和分类。
我们发现，最具预测性的词汇勾勒出了性侵和虐待揭露的生动原型。然后我们估计，在运动的第一周，11%包含“MeToo”的全新英文推文披露了发帖人性侵或虐待经历的细节，5.8%披露了此类事件的早期生活经历。我们研究了性侵和虐待推文发布者的人口统计学构成，发现25至50岁的白人女性在推特上的占比过高。此外，我们发现性侵和虐待个人经历的大量分享传播范围广泛，在运动的第一周，600万至3400万推特用户可能看到了他们关注的人发布的此类第一人称揭露内容。
这些数据表明，所分享的揭露内容不仅限于承认曾遭受性骚扰，还常常包括对性侵和虐待早期生活经历的生动且创伤性的描述。这些发现和方法凸显了在新颖机器学习方法支持下的内容分析的价值，有助于我们更好地理解这些揭露的广泛程度，这可能放大了“MeToo”运动的传播范围和显著性。
