San Francisco State University, San Francisco, CA, USA.
J Interpers Violence. 2022 Aug;37(15-16):NP13603-NP13622. doi: 10.1177/08862605211001481. Epub 2021 Apr 12.
Online social media movements are now common and support cultural discussions on difficult health and social topics. The #MeToo movement, focusing on the pervasiveness of sexual assault and harassment, has been one of the largest and most influential online movements. Our study examines topics of conversation on Twitter by supporters of the #MeToo movement and by Twitter users who were uninvolved in the movement to explore the extent to which tweet topics for these two groups converge over time. We identify and collect one year's worth of tweets for supporters of the #MeToo movement ( = 168 users; = 105,538 tweets) and users not involved in the movement ( = 147 users; = 112,301 tweets referred to as the Neutral Sample). We conduct topic frequency analysis and implement an unsupervised machine learning topic modeling algorithm, latent Dirichlet allocation, to explore topics of discussion on Twitter for these two groups of users before and after the initial #MeToo movement. Our results suggest that supporters of #MeToo discussed different topics compared to the Neutral Sample of Twitter users before #MeToo with some overlap on politics. The supporters were already discussing sexual assault and harassment issues six months before #MeToo, and discussion on this topic increased 13.7-fold in the six months after. For the Neutral Sample, sexual assault and harassment was not a key topic of discussion on Twitter before #MeToo, but there was some limited increase afterward. Results of bigram frequency analysis and topic modeling showed a clear increase in topic related to gender for the supporters of #MeToo but gave mixed results for the Neutral Sample comparison group. Our results suggest limited shifts in the conversation on Twitter for the Neutral Sample. Our methods and results have implications for measuring the extent to which online social media movements, like #MeToo, reach a broad audience.
在线社交媒体运动现在很常见，支持关于困难的健康和社会话题的文化讨论。#MeToo 运动是规模最大、最有影响力的在线运动之一，其重点是性侵犯和性骚扰的普遍性。我们的研究通过 #MeToo 运动的支持者和未参与该运动的 Twitter 用户在 Twitter 上的对话主题，探讨了这两个群体的话题在多大程度上随着时间的推移而趋同。我们确定并收集了一年来支持 #MeToo 运动的用户（=168 名用户；=105538 条推文）和未参与该运动的用户（=147 名用户；=112301 条推文，称为中立样本）的推文。我们进行了主题频率分析，并实施了无监督机器学习主题建模算法，潜在狄利克雷分配（LDA），以探讨这两个用户群体在最初的#MeToo 运动前后在 Twitter 上的讨论主题。我们的研究结果表明，与中立样本相比，#MeToo 的支持者在 #MeToo 之前讨论的主题不同，并且在政治方面存在一些重叠。支持者在 #MeToo 之前的六个月就已经在讨论性侵犯和性骚扰问题，并且在之后的六个月里，这一话题的讨论增加了 13.7 倍。对于中立样本，在 #MeToo 之前，性侵犯和性骚扰并不是 Twitter 上的一个主要讨论主题，但之后也有一些有限的增加。二元词频率分析和主题建模的结果表明，#MeToo 的支持者的主题与性别相关的话题明显增加，但对于中立样本的对比组，结果则喜忧参半。我们的研究结果表明，中立样本在 Twitter 上的对话变化有限。我们的方法和结果对于衡量像#MeToo 这样的在线社交媒体运动在多大程度上能够吸引更广泛的受众具有重要意义。
