I recently tweeted out a network graph based on the Twitter account of Jo Caulfield, a stand-up comedian and comedy writer. It is a very impressive graph for a single Twitter user, and Jo was also taken by the graph, so I thought I’d write a short blog post explaining what it all means.
The network graph, below, represents a network of 871 Twitter users whose recent tweets contained “Jo_Caulfield”, or who were replied to or mentioned in those tweets.The tweets in the network were tweeted over the 9-day, 1-hour, 57-minute period from Friday, 01 July 2016 at 08:01 UTC to Sunday, 10 July 2016 at 09:59 UTC.
The network graph is made up of several groups of Twitter users, and the groups are determined by the content of tweets. Group 1 (on the left hand side with Jo in the centre) displays the Twitter audience of Jo Caulfield, which is known as a Broadcast Network. This contain an audience of people who are linked only to Caulfield’s account (see Smith, Rainie, Shneiderman, & Himelboim, 2014). In this group the most frequently occurring words include:
By navigating to the graph gallery version of the graph and looking for metrics related to this group e.g. “Top URLs in Tweet in G1′ it is possible to examine metrics by group level. Within each graph it is also possible to contrast the different groups, this is particularly useful when the contrast illustrates a divergent view or market segment. For instance, in the graph above we can see that group 2 is a secondary Broadcast Network centred on the Twitter account of @pperrin. Other groups are focused on different topics, and involve fewer users and denser discussions.
In this post I would like to highlight interesting statistics overall in the graph.
Three most popular URLs consist of:
-  https://twitter.com/Jo_Caulfield/status/751734555191705600
-  https://twitter.com/jo_caulfield/status/751734555191705600
-  http://blog.jocaulfield.com/2016/07/happy-4th-of-july-mr-springsteen.html
Three most frequently used hashtags consist of:
Three most frequently occurring co-words consist of:
Three most frequently occurring domains consist of:
Three most mentioned users consist of:
Three top tweeters consist of:
Three most replied to users consist of:
I could delve into many further aspects of the graph, but I’d like to point you to the NodeXL graph gallery which contains a comprehensive overview of the analytics overall, and by group level.
I’d highly recommend carefully examining Figure 3 from Smith, Rainie, Shneiderman, & Himelboim, 2014 (copied below), which provides a guide in contrasting patterns within network graphs:
Do you have any questions or are you interested in examining your own network graph? Feel free to drop me a message (@was3210). Thanks to the Connected Action team for producing this graph, and thanks to Neil Erskine from Byline Analytics for suggesting this post. No data was captured and/or analysed, at any time, in the production of this blog post.