On Oct. 15, 2017, actress Alyssa Milano sparked a firestorm on social media when she asked her Twitter followers to reply "me too" if they had ever been sexually harassed or assaulted. (Social justice activist Tarana Burke founded the "Me Too" movement more than 10 years ago as a way to help sexual assault survivors heal.) What followed were 1.5 million responses—many from sexual assault survivors sharing their experiences, others from people showing support and some from critics—all using the hashtag #MeToo.

Meanwhile, San Francisco State University Assistant Professor of Economics Sepideh Modrek watched the movement unfold online from her home. Her Twitter feed filled up with friends and acquaintances disclosing details of their own abuse, she says, and something compelled her to start archiving the tweets. One night she stayed up until 2 a.m. taking screenshots of #MeToo tweets, ultimately compiling 400 pages of shots. She didn't know it at the time, but this would be the foundation for her latest research project.

"I was floored that people were sharing details. They were writing things like, 'When I was 15, this happened,'" she said. "I was seeing pretty intimate details being shared in a public forum in a way I'd never thought people would do. I was impressed and captivated."

Her research, published Sept. 3 in the Journal of Medical Internet Research, is a snapshot of the online movement during that first week when it reached critical mass. With the help of machine learning, Modrek and her research assistant Bozhidar Chakalov studied more than 12,000 #MeToo tweets posted between Oct. 15 and 21. After applying and gaining access to Twitter's application programming interface, or API, they were able to count every undeleted #MeToo tweet. They then downloaded a representative subset, which helped them describe magnitude of the movement in terms of size, demographics and the personal narratives shared.

They found that 11% of all novel tweets (meaning tweets that weren't retweets, replies to other replies or messages with pictures or links) included a revelation of sexual assault or abuse. Nearly 6% of those incidents occurred early in life (any time before the age 22).

The majority of people sharing were white women between the ages of 25 and 50. What's striking is that women were reporting these events 20 to 30 years after they happened, Modrek says. "They still remember it. There's clear enduring trauma associated with each disclosure," she said.

Modrek's analysis also showed that voices were systematically missing. For example, African American women were less likely to disclose details on Twitter, but other data shows that they are equally or more likely to have experienced sexual abuse or assault.

To be included in their data pool the #MeToo tweets had to meet certain criteria. They had to be novel, in English and geotagged in the U.S. This shrank their data set from 1.5 million to just over 12,000 tweets. Part of the reason for the parameters was so they could hone in on tweets that contained personal disclosures of sexual abuse.

It's been nearly two years since the start of the #MeToo movement, the largest public discourse on sexual violence in history. A lot has changed in that time, says Modrek, including some people's perceptions of the movement. But she hopes her research will remind people of the movement's significance and the possibility that (as she wrote in her research) it helped bring about a "deeper understanding of the prevalence, early life experience and enduring trauma of sexual assault and abuse."

"A lot of people spoke up and publicly shared these experiences," she said, "and it completely changed our dialogue. I wanted to capture and honor their courage."

More information: Sepideh Modrek et al, The #MeToo Movement in the United States: Text Analysis of Early Twitter Conversations, Journal of Medical Internet Research (2019). Journal information: Journal of Medical Internet Research Sepideh Modrek et al, The #MeToo Movement in the United States: Text Analysis of Early Twitter Conversations,(2019). DOI: 10.2196/13837