During 2012, we collected data relating to a list of 854 international Rugby Union players, clubs, and organisations currently active on Twitter. These accounts were manually assigned to 15 overlapping communities, each corresponding to a different country (Argentina, Australia, Canada, England, Fiji, France, Ireland, Italy, New Zealand, Samoa, Scotland, South Africa, Tonga, USA, Wales). In the case of players, they can be assigned to both their home nation and the nation in which they play club rugby (players assigned to two different countries are indicated by light grey nodes).

In total, we collected ~1.2 million tweets, ~6k user lists, and ~36k follower links within the set of 854 users. From the tweets, we extracted mention and retweet information. By combining these different "views" of the data using a rank aggregation method, we constructed a "unified" graph representation of the relations between the Twitter accounts, which preserves the most informative underlying associations between users in the original views. A detailed description of the methodology is provided in this paper.

Below is a visualization of the unified graph representation for the users in the data. Users are coloured according to their community (i.e. country). The size of each node is proportional to its in-degree (i.e. number of incoming links). The visualisation reveals sub-groups corresponding to individual clubs based in different countries (e.g. at the bottom-right, we see sub-groups corresponding to Leinster, Connacht, Ulster, and Munster). Also, we see that certain nodes lying between communities correspond to players who have recently transferred between clubs located in different countries.

By rolling over a node with the mouse, you can view the node's corresponding Twitter screen name and hide all nodes and edges, apart from the ones that are connected to the highlighted node. Left clicking on a node will open the user's Twitter page in a new window.

[Download GEXF File]   [Datasets]   [Paper]