Like many institutions of higher education these days, the University of Maine at Augusta communicates about its accomplishments and keeps track of the work of others using the social media service Twitter. In its communications, UMA traces the paths of the community that surrounds it.
Unlike the social media platform Facebook (oriented toward friend and family relationships) or Pinterest (devoted to the sharing of images), Twitter acts like a news clipping service of sorts. Limited to 140 characters of text, Twitter posts are like headlines in a newspaper, with links to web pages containing more information. Making headlines social, Twitter posts can mention other Twitter accounts that are relevant to the story. By tracking those mentions, we can find communities of posters who find one another’s work relevant.
To generate the social network graph you see below, I’ve searched through all Twitter posts made this year by the university’s official account, @UMAugusta, and identified all of the other Twitter accounts that @UMAugusta has mentioned. In a second step, I looked at the records of each of the Twitter accounts @UMAugusta mentioned and found out whether and how often they referred to one another. The result, formally speaking, is a level 1.5 ego network. In the graph below, Twitter accounts are indicated with labeled dots; in the parlance of social network analysis, these are called “nodes” or “vertices.” The larger a dot is in the graph, the more often it is mentioned by other Twitter accounts. Mentions between Twitter accounts are indicated with curved lines, which network analysts refer to variously as “lines,” “arcs,” “edges” or “ties.” The darker a line is, the more often mentioning occurred between two Twitter accounts.
To highlight structure in the network of mentions surrounding @UMAugusta, I identified five clusters of Twitter accounts who mentioned one another especially often. These clusters are color-coded in the network graph above. Because the identification of clusters of conversants was driven by data, not by pre-conceived notions about which accounts might “naturally” be grouped together, it is curious to see how particular clusters focus on particular domains. Some patterns:
- The dark green cluster in the lower-right of the graph consists strongly of offices and officers connected to student life and services at the University of Maine at Augusta.
- The dark blue cluster in the upper-left of the graph is anchored around newspapers and newspaper reporters of central and southern Maine — the Portland Press Herald, the Kennebec Journal of Augusta and the Morning Sentinel of Waterville. These three newspapers are not simply tied by geography, but are also published under the aegis of the MaineToday Media company; @centralmesports is a joint outlet of the Kennebec Journal and Morning Sentinel. Other central Maine institutions — Colby College and the Holocaust & Human Rights Center — are also featured in this cluster.
- The light green cluster in the lower-left of the graph features strong representation in the arts, with the 5 Rivers Arts Alliance, Harlow Gallery, photographer Jill Guthrie, and The Band Apollo included.
- Immediate substantive commonalities in the red upper-right cluster, including my own account, the Maine State Library, the Maine Humanities Council and a edu-metrics website NerdScholar are elusive. We are tied to one another because of our mutual communications across disciplinary boundaries.
- The light-blue cluster at the bottom of the graph is a remainder category, consisting mostly of Twitter accounts that UMA has mentioned but that do not mention other accounts often.
- Finally, although these clusters identify groups of accounts that communicate more often internally, connections between clusters are frequent, indicating that most of the accounts mentioned by the University of Maine at Augusta are part of a broader community.