One of the data analysis methods that I’m learning about in the Data, Analytics and Learning MOOC is social network analysis (SNA). As the name suggests, SNA investigates social processes and interactions, rather than looking at numerical data. SNA has applications in many different disciplines, and it doesn’t have to concern itself only with humans. Any system where there is interaction between distinct entities could be analysed using SNA. All that is required is to have some “actors” i.e. the individuals or entities within the network, and “relations” i.e. links between actors.
There are a wide range of potential data sources for SNA. In a learning context (which this MOOC is focussed on) data could be obtained from Twitter to see how students in a particular class or unit are interacting on that platform, or from the interactions on discussion forums within a Learning Management System (LMS).
There are several measures of a social network that can calculated. Some of them relate to the size of the network e.g. diameter, or the “connectedness” of the nodes within the network e.g. degree centrality and closeness centrality. It’s also possible to investigate the modularity of the network, which looks at whether there are smaller modules, or communities, within the network.
I’m keen to have a go at exploring SNA, especially with regards to Twitter networks. I’m working on a research project looking at Twitter use at conferences, and I think the tools and measures that I’ve learnt about will be useful for this project. As far as library-specific use of SNA is concerned, I’m having trouble coming up with possible uses for it. Most of our systems produce numerical data about items or people – there aren’t many networks involved. Unless there was a librarian who was involved in teaching a unit which had a presence within the LMS, I’m not sure how else libraries could take advantage of SNA. Maybe by the end of the MOOC I might have some more ideas.