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README.md

Project Title: An Exploration of the Social Movement #metoo

How can we begin to understand the breadth of the #metoo movement? In this visualization, explore the results of k-means clustering and the themes discovered among 1.4 million tweets.

Features

  • Discover the peaks and valleys of #metoo data over a 6 month period
  • Learn about k-means clustering
  • Explore cluster top words
  • Uncover the tweets within the themes of the #metoo movement

Abstract

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?”

Creators and Special Thanks

This project was completed by Ellie Frymire, with fantastic help and direction from both Daniel Sauter and Aaron Hill at The New School

metoo_3.2

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