JMIR Publications

ABSTRACT

Background:

The #MeToo movement sparked a national 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.

Objective:

The intent of this study is to document, characterize and quantify the early public discourse and conversation of the #MeToo movement from Twitter data. We focus on posts with public first-person revelations of sexual assault/abuse and early life experiences of such events.

Methods:

We purchased full tweets and associated metadata from the Twitter Premium API between October 14th –October 21st, 2017; 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 regressions 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.

Results:

We show that the most predictive words create a vivid archetype of the revelations sexual assault and abuse. We then estimate 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 examine the demographic composition of posters of sexual assault and abuse and find that white women aged 25-50 were overrepresented relative to their representation on Twitter and national estimates in posting about their experiences. Furthermore, we find 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 revelation from someone they follow in this 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 finding 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.