Current Trends in Social Computing (IS30290) was one of the most interesting and engaging classes I took as part of my Masters in Library and Information Studies (MLIS). It explored issues relating to social media and social networks, but also the characteristics of networks, data reuse, social capital, characteristics of infrastructure, user generated content, memes, crowd sourcing and the social impacts of social computing. Our lecturer really brought the topics and issues to life and encouraged critical thinking. I loved it.
One of the tools for evaluating social media explored during the course was NodeXL. The program itself was not without it’s problem but once I got the hang of it it revealed some interesting results.
Mapping my Facebook network
Vertices and edges in this graph represent my Facebook friends and the friendship links between them. The different colours represent different groups of friends, using the Fruchterman-Reingold layout, with the vertex size representing the number of connections, or degree, that person has within the network.
Using the Fruchterman-Reingold layout I can clearly see the groups of people within my friends who also know each other. This groups that have appeared are uncannily accurate. It comes as no surprise that groups of friends in the real world should translate into groups of friends in the online world, however the first time I saw the graph appear I admit that it felt a bit – spooky. The vertex size represents the number of connections, or degree, that person has within the network, so those that have more connections are represented by larger dots.
By degrees my husband is the most important person in my graph and he is located among my ‘local friends’. This is somewhat reassuring. The next most important people by degree are all members of staff at the animal shelter (DSPCA) where I volunteer. This group is represented by orange dots. These people are also the most important by Closeness Centrality. The people within this group also have a very high degree of connectedness as they are all friends with each other. This is a very tight group of people (other volunteers and staff) who are passionate about animal welfare.
The largest group in the graph (represented by red dots) represents what I can best describe as my local friends. These are people are friends that I went to school with or people that I have known since I was quite young. There are a lot of connections between people in this group with some of these friends having a very high degree of connectedness.
Other groups that have formed are the friends I made while studying for my undergraduate degree in Trinity College. There are some links between this group and my local friends as can be seen in the graph. Other groups include some of the knitters (dark green) that I know through my hobby and a group of people that I met when I spent a year in France on Ersamus (pale blue).
The final two groups of interest are my family represented by the blue vertices and the friends that I have made through the MLIS, called SILS on the graph and represented in lime green. There is one single link between my SILS friends and the rest of my graph. It turns out that one my fellow students knows one of my cousins’ wife. This was a surprise as this was a connection I wasn’t aware of before this.