by Luca on 22/09/10 at 9:00 am
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.