Social Network Looking to Invoke Political Change
Sean Parker has been working on an app that he is hoping will help influence and even prompt public policy. The basic idea behind the app is that each user is presented with a social or political issue such as “Should marijuana be legalized?” and is prompted to either press agree or disagree. The user is then presented with the percent of people who clicked agree and disagree. The app is called Brigade and Sean Parker is saying that once the app reaches a larger user population the app will be able to help users that voted the same on a particular issue to connect and hopefully band together to appeal to their local governments. Each “voter” will also be given more credibility by having to go through a registration process that confirms that they are indeed a local registered voter. I think that for an app like this to really make a political or social impact it will need to encompass a large social graph of a local area, or even a whole state. Only then will people be able to accurately predict political elections/voting issues and influence legislation in a meaningful way.
Now I will talk about how the growth of this app is related to networks and also how in the future this app may relate to networks. This app is an example of how a social network forms, and also how it can influence its nodes and edges. Sean Parker is hoping that this app spreads like a social contagion. Taking advantage of local bridges in a social network could be one way to spread an app quickly and efficiently. One way to take advantage of local bridges is by promoting the app city by city. The users in this app are all part of a social network, where the nodes are the users and the edges are users who have connected with each other about a particular issue. Because edges form between users not only if they are friends but also if they connect over a political issue, there may be a high number of edges for a social network. If this app becomes very popular I could see a social graph with a very high neighborhood overlap and a high level of embeddedness. It will be interesting to see if a graph like this will have a higher rate of growth than a graph with many local bridges. It will also be interesting to see the cascading effects a social network like this would have. For example, if a user sees that their opinion on an issue is heavily outweighed by the opposing side how likely would it be that they change their opinion on the issue. Also, what percent of the population would have to be against a user’s opinion for the user to change his or her opinion? It will also be interesting to see how a user’s strong ties, such as friends or family, influence their opinions on political issues. It would seem that the more strong ties a user has connected to that particular issue, the more likely the user is to change his or her mind. There would also be entirely separate social graphs encompassing the group of people who voted on a particular issue. These graphs would also be considered to be balanced graphs as you could separate the graph into two opposing groups, those who voted agree and those who voted disagree. In these graphs the edges could be labeled positive or negative depending on if the two nodes agree on a particular issue or not.