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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.
People - Details - Things - Intimate - Details
"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,...
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