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How Diffusion in Networks Explains the Spread of the #MeToo Movement

https://www.telegraph.co.uk/news/world/metoo-shockwave/

In October 2017, the New York Times reported allegations of sexual assault against the famous Harvey Weinstein. Just a few days after the article was published, the hashtag #MeToo went viral. It was first used by activist Tarana Burke, and then adapted by actress Alyssa Milano to encourage survivors to come forward as a way of showing the world how widespread the problem was. At first, some more high profile scandals broke out: Larry Nassar, Kevin Spacey, Aziz Ansari. All of a sudden, hundreds of thousands of women across the world began telling stories about sexual abuse, assault, and rape. A few years ago a revolution like this was unimaginable. It is not that the problem wasn’t as widespread a few years ago, but rather that women now feel empowered enough to come forward. According to the article “Globalisation, connectivity, and the women’s rights movement have created the perfect storm”. The article goes on to detail the role of social media and online networks in fuelling the movement. As increasingly more women came forward, other women started to see that they were not alone. Social media provides a safe platform on which women can share their stories without fear, and where they can connect with other survivors and feel confident as a unified front. We can use what we just learned about cascading behavior in networks to understand why women switched from the decision to stay silent to the decision to come forward with their stories.

In class, we set up a model in which there is a given network, and each node has to make a decision. In our case, the decision is come forward (A) or not to come forward (B). Decisions in the model are based on who you are connected to – people tend to model the behavior of their neighbors. Nodes change their decisions when the fraction choosing A (p) is greater than the threshold q = b/a+b. That is, if the fraction of your friends adopting A is at least your threshold, then you will choose A. Otherwise you will stick with B. In the case of the Me Too movement, we can start with most people at B, and say that one node, maybe Tarana Burke or Alyssa Milano, switches to A. This could allow for a single node connected to Alyssa Milano to switch to A as well because the fraction of people adopting A has now gone above q for that node. Once those nodes switch, more nodes whose fraction of neighbors who switch to A is at least their threshold will switch to A. If the number of people I am friends with who start coming forward exceeds my threshold, I will also come forward. Some people who remain with B are people for whose fractions of neighbors switching to A is below their threshold. If my threshold for coming forward is very high, or if not a lot of people I know have come forward, then I will choose not to come forward. We also established in class that it is difficult to get people to switch across clusters. If we consider a cluster of very feminist, activist, vocal women who are connected to each other, and a cluster of women who are less so, then it would make sense how the first cluster tends to go mostly A and the second cluster tends to go mostly B.

Of course, this is a simplified model, but it is a very interesting way to think about how diffusion in networks can be applied to something as prominent and important as the MeToo movement.

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