## Network Diffusion – The Ice Bucket Challenge

The phenomenon that I am going to be talking about is going to be the Ice Bucket Challenge that went ‘viral’ a few years ago. The two articles I will be referencing are “Ice Bucket Challenge: 5 things you should know” by Amanda Trejos and “Here’s How the ALS Ice Bucket Challenge Actually Started” by Alexandra Sifferlin. In the second article, by Alexandra Sifferlin, the author goes on to explain how the Ice Bucket Challenge even started. It started off with a single person posting a video on Facebook as a joke and to support his long time best friend who had ALS. It started spreading through their small town of Pelham, N.Y and eventually it reached Peter Quinn in Boston, who had recently been diagnosed with ALS, via a few mutual friends. From there it spread to a big influencer called Pete Frates, and with Frates’ huge social following it took off into a huge social trend. We can see in the first article that this social trend grew so large that 17 million people participated worldwide with 2.5 million of those people being within the United States.

This social phenomenon relates to the network diffusion that we learned about in class, particularly to clusters in the network and how ideas transfer between clusters. In this example, the challenge started in a small town, and since the initial converter had a lot of social connections it spread quickly throughout the town. However, it could have very easily stopped there, but it managed to actually jump clusters when it went from the small town of Pelham N.Y, to the new cluster surrounding Peter Quinn, then Pete Frates, then as it spiraled out into the whole world.

The one interesting difference between the generic model we discussed in class versus our real world example is that in our generic model we assume 2 major things: firstly we assume that every node has the same basic value when it comes to the value of the two choices A and B, and secondly that every node associates the same value of A and B with every other node. To give an example of this, if we imagine our graph and have nodes X and Y, it is possible that X values option A more than Y does. In our real world example this would be represented by someone like Peter Quinn or Pete Frates who both suffer from ALS and so they will have a higher value in doing something like the Ice Bucket Challenge than someone who does not even know what ALS means. For the second statement the example would be that a person X will be better inclined to switch to option A if their close friend has switched as opposed to someone they know in passing. With these considerations it is still very interesting to see how well our generic model can represent the real world happenings, and this shows how networks applies to everyday situations.