Recently we’ve posted on Friendfeed a visualisation of Italian Friendfeed users extracted from the data we’ve collected in 2009. Since the map started an interesting debate (you can read it here – in Italian –) we thought to write a short post explaining how the map has been done and what are its limits and possibilities.
The map is based on a network made of 8024 nodes with 244542 edges (even if the map shows only the nodes with more than 147 followers but statistical values have been counted on the whole network).
We collected the data in September 2009 starting from all the public messages posted on Frienfeed (you can read more about this in out SBP10 paper).
We processed the network with Gephi and the map shows the indegree value as node size and betweenness centrality value as node colour.
The final result is rather interesting since it shows on one side a group of huge nodes with many followers but at the same time it shows how the is no simple correlation between the number of followers and the betweenness centrality value. Since BC value is often used to identify relevant nodes or hidden hubs this can be read as the quality of your connections matters more than their number.
Nevertheless a final remark has to be done. Metrics like betweenness centrality works really well in traditional networks but they fail to grasp the new conversational nature of Friendfeed Network (but the same could be said about Twitter). In Friendfeed conversations exist often out of the network made by the following/follower structure. When I get in touch with a message originally produced out of my network only because a friend mine comments on it, what happens is the creation of an actual network based more on social behaviour than on the underlying set of connections.
We need new social metrics.
Categoria: Highlights
Twitter @reply Networks on UK General Elections #UKGE2010
Few days ago Axle Burns and the people from “Mapping online publics” posted a very interesting article about mapping the Australian election following #ausvote tweets. The idea behind that was rather good and simple: by mapping all the messages containing the conventional reply symbol (@username) one could map the conversational network surrounding a specific topic (defined by the #hashtag). Of course this methods has some limitations (clearly explained by Axle), nevertheless it can be use to produce a rough map of the conversational network.
Since some time ago we’ve downloaded (using Twapperkeeper – the same service used by Axel) all the tweets with the hashtag #ukge2010 (the “official” hashtag about Uk general elections) we have decided to do the same analysis on Uk tweets.
So we’ve got the @replies network and using gephi we counted the indegree and the betweenness centrality of the nodes. Following Axle we’ve also excluded from the visualisation itself any users who received fewer than 100 @replies.
Here you can see the result:
The size of the nodes represents the indegree value while the colour represents the betweenness centrality. This is the table showing the top values:
What can be easily noticed is that most important nodes within the conversational network are not the official twitter account from political parties or politicians. Bloggers, journalists and consultants like @carlmaxim or @bengoldacre get more direct replies than official twitter account from politicians – like @nick_clegg – or from political parties – like @UKLabour-.
This can be due to the use that those users make of Twitter, @nick_clegg or the @UKLabour probably are not perceived as someone you would reply or address directly.
At the same time it is important to highlight how there is no clear correlation between indegree and betweenness centrality, users like @bloggerheads have a high betweenness centrality value but a very low indegree. This can be surely due to a different behaviour of users (outdegree value is important in how betweenness centrality is defined) but at the same time I think that betweenness centrality, even if is a standard measure for sna, is unable to get the real complexity of a conversation network connected through a Twitter #hashtag (but we’re coming back on this point very soon).