In late 2012, we collected data relating to the Twitter accounts of 419 Members of Parliament (MPs) in the United Kingdom, as listed on Tweetminster. These accounts were manually assigned to five disjoint communities, according to their political party affiliation (Conservative, Labour, Liberal Democrat, SNP, Other).

In total, we collected ~540k tweets, ~3k user lists, and ~27k follower links within the set of 419 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, produced using Gephi and sigma.js. Users are coloured according to their community (i.e. political affiliation). The size of each node is proportional to its in-degree (i.e. number of incoming links).

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.