Mapping the Digital Humanities Community via Twitter
http://www.tandfonline.com/doi/full/10.1080/23311983.2016
The article above analyzes what it calls the “digital humanities community”. The definition of this term matters less than the fact that it is a very particular label, and only a tiny subset of the enormous Twitter community would proclaim to have. In particular, the article analyzed about 2500 Twitter accounts with self-proclaimed “digital humanities” labels in their bios, and found that such a small community would necessarily be extremely tight-knit.
What is perhaps more interesting is how the article analyzes the structure of this dense, small network within the larger Twitter network. The authors propose the concept of “betweenness centrality”, which measures the number of times a vertex (a member of the digital humanities community) is present on the shortest path between two other vertices. The intention is that the metric highlights users who are structurally in a “bridge” position between the subdivisions of the network, and indeed it finds very specific users with large numbers of followers that form the bridge between otherwise disconnected subdivisions of an already small network.
Another metric, the “eigenvector centrality”, assigns each vertex a score of authority that is based on the score of the vertices with which it is connected. The motive is to highlight the extremely connected users within the larger group of the network, and reveals an interesting “nucleus” that provides an interesting complement to the findings from the betweenness centrality metric, and maybe provides some insight as to how this community is structured.
The analyses in this article are similar to the basic methods of structural analysis we covered so far, such as strong and weak ties. Moreover, it employs two different metrics of analysis that interestingly confirm two sides of what we would have assumed so far about small online communities (i.e. tight-knit and dominated by popular users).