Background: 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.

Methods:Voyant tool
We use tweets from the period of 15 Oct, 2017 -31 March,2020.
The tweets were categorized along two dimensions: if they disclosean experience of sexual assault and abuse
We used machine learning methods to summarize and classify the content of individual tweets with revelations of sexual assault and abuse.
Results: We found that the most predictive words created a vivid archetype of the revelations of sexual, harassment, woman, story, victim and campaign. Twitter creates a big platform for users to publish their opinions with rare restrictions, which meet the basic meaning of public sphere. Secondly, the case study comes from an online public sphere and causes heated discussion because the movement involved famous people, especially on some issues, which affect public opinion to some extend.
Conclusions: These data illustrate that revelations shared went beyond acknowledgement of having experienced sexual harassment and often included vivid and traumatic descriptions of assault and abuse. These findings and methods underscore the value of content analysis, supported by machine learning methods, to improve our understanding of how widespread the revelations were, which likely amplified the spread and saliency of the #MeToo movement.