An Exploration of the Movement #metoo by Ellie Frymire

Following the wake of several women coming forward against Harvey Weinstein, on October 15th, 2017, Alyssa Milano started an online movement behind the hashtag #metoo. She posted, “If you’ve been sexually harassed or assaulted write ‘me too’ as a reply to this tweet” (@Alyssa_Milano). What followed was a flood of stories, building a community of support, natively and primarily through social media. The movement encouraged more women to come forward — not only validating the experience of victims, but exposing more perpetrators beyond Weinstein. But is that all that was said within #metoo? How can we leverage this public platform to take the pulse of the crowd? This project explores the text of tweets in the 6 months following the start of the hashtag, using unsupervised machine learning to derive organic themes. By analyzing the scope of language used in #metoo tweets through k-means cluster analysis, we can uncover hidden themes. The project aims to answer the question: “what are people really saying with #metoo?”