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 (N = 168 users; N = 105,538 tweets) and users not involved in the movement (N = 147 users; N = 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普遍，并支持关于困难的健康和社会话题的文化讨论。#MeToo 运动专注于性侵犯和性骚扰的普遍性，一直是规模最大、最具影响力的在线运动之一。我们的研究检查了#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，以探索这两个群体的推文主题随着时间的推移在何种程度上融合。我们为#MeToo 运动的支持者（N = 168 名用户；N = 105,538 条推文）和未参与该运动的用户（N = 147 名用户；N= 112,301 条推文，称为中性样本）。我们进行主题频率分析并实施无监督机器学习主题建模算法，潜在 Dirichlet 分配，以探索这两组用户在初始 #MeToo 运动前后在 Twitter 上的讨论主题。我们的结果表明，#MeToo 的支持者与 #MeToo 之前的 Twitter 用户的中性样本相比，讨论了不同的话题，在政治上有一些重叠。在#MeToo 之前六个月，支持者已经在讨论性侵犯和性骚扰问题，而在之后的六个月里，关于这个话题的讨论增加了 13.7 倍。对于中立样本，性侵犯和性骚扰在#MeToo 之前不是 Twitter 上讨论的关键话题，但之后的增长有限。二元组频率分析和主题建模的结果显示，#MeToo 的支持者与性别相关的主题明显增加，但中性样本对照组的结果好坏参半。我们的结果表明，中性样本在 Twitter 上的对话变化有限。我们的方法和结果对于衡量#MeToo 等在线社交媒体运动触及广大受众的程度具有重要意义。