What happened to Michael Vick ?!
(Network)
http://twitpic.com/6nyr0v
(Degree plot)
http://twitpic.com/6nys2o
During September 18, 2011, the quarterback of the Philadelphia Eagles, Michael Vick, was injured. Who would replace Vick? The more interesting question is if someone cannot watch the game then where are people on the web retrieving their information about Vick's replacement? I sought out a solution to this problem: The first step was to find a media outlet that made information public. Twitter, Facebook, Google+ are some examples of social networks that release information but Twitter turns out to be the easiest network to gather information. After performing some programming twists and turns, I was able to figure out the most tweeted topics on twitter at that moment in time. Among them were #ThingsThatGetMeUpset, #ReasonsWeCantBeTogether and the third most trend was Mike Kafka, the Eagles back up quarterback! On a side note, due to an award program occurring, Modern Family and Outstanding comedy series were among the top... just to give you an idea of the type of trends occurring that night. I expected Mike Kafka to be a trend but I did not expect Mike Kafka to be the third most popular trend on twitter; the NFL must have a very strong representation in the social network. Now came the interesting part. I performed more twists and turns and grabbed the retweets involving the name 'Mike Kafka'. I assumed that people retweeting his name were not watching the game and were showing their followers (other potential people not watching the game) that 'person X just said that Michael Vick was injured and Mike Kafka is coming in'. There are two plots attached- the first link is a graph we have seen many times in class and it shows where people were getting their information from. The sample size is around 650 retweets. As it should come to no surprise, EagleInsider was the source of information for many people. There are also separate islands occurring on the plot where nodes are retrieving information from different sources and everyone in the island is not connected to people in other islands. There is no evidence of triadic closure because this plot was produced at one instance in time. If I consistently gathered information then triadic closure would most likely occur. We do see occasional retweets from nodes connected to the main source: There is a primary source of information (EaglesInsider) and nodes connecting to the primary source serve as a secondary source of information for other nodes (there are only a couple of instances of this behavior). Essentially, the graph is relatively disconnected because of the nature of Twitter. To give a few values I determined (through twists and turns): there are 110 different islands, 363 nodes and 267 edges. Dividing the nodes by the edges should give an good indication oh the average degree of a node. For example, with our values 363/267=1.36 nodes per connection. It is best to interpret this ratio as every node makes only one connection. Which makes sense because people are interested in only one source of information to retweet in this type of situation. I produced a Degree plot showing the various degrees for each node (see the second link). Most of the values have a degree of only 1 meaning only 1 connection was made and there are a few cases of higher degrees (the highest degree is from EagleInsider) which are highly connected but the average is about 1 connection per node. To conclude, I was able to figure out the main source of retweets during Michael Vick's tragic injury and determine that there was a lot of disjointed nodes. The highly connected node was my main focus because that is the person that probably has the most reliable information about the Eagles on Twitter for people not watching the game. The disjointed nodes are getting information from other sources that probably have less reliability due to the degree. In a more serious topic like politics, this type of analysis could be performed to determined who people trusted or maybe even distrusted (depending on the situation). The important thing that comes from analyzing data from social networks is the spread of information. I was only interested in the sources spreading the information and analyzing the behavior at one instance in time. A more comprehensive analyzes would include multiple time periods to determine the flow of information. Side note: the tweet from EagleInsider was “EaglesInsider: Michael Vick exiting game w/unknown injury ... Mike Kafka entering game on first-and-goal from the 9”. Cheers!